Ethical Guidelines on the Use of Artificial intelligence (AI) and Data in Teaching and Learning for Educators PDF

Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for Educators Manuscript completed in September 2022 The European Commission is not liable for any consequence stemming from the reuse of this publication. Luxembourg: Publications Office o...

Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for Educators Manuscript completed in September 2022 The European Commission is not liable for any consequence stemming from the reuse of this publication. Luxembourg: Publications Office of the European Union, 2022 © European Union, 2022 The reuse policy of European Commission documents is implemented based on Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 4.0 International (CC-BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of elements that are not owned by the European Union, permission may need to be sought directly from the respective rightholders. Print ISBN 978-92-76-57539-9 doi:10.2766/127030 NC-07-22-649-EN-C PDF ISBN 978-92-76-54190-5 doi:10.2766/153756 NC-07-22-649-EN-N Acknowledgements The guidelines were developed by the European Commission, with the support of the Expert Group on Artificial Intelligence and Data in Education and Training, led by consultants associated with ECORYS. The Commission would like to thank the following: Agata Majchrowska Aleksander Tarkowski Ari Alamäki Deirdre Butler Duuk Baten Egon Van den Broek Guido Noto La Diega Hanni Muukkonen van der Meer Inge Molenaar Jill-Jênn Vie Josiah Kaplan Juan Pablo Giraldo Ospino Julian Estevez Keith Quille Lidija Kralj Lucilla Crosta Maksim Karliuk Maria Wirzberger Matthew Montebello Stephan Vincent-Lancrin Tapani Saarinen Tobias Rohl Viola Schiaffonati Vitor Hugo Mendes da Costa Carvalho Vladislav Slavov 4 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I)  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 5 Contents Foreword6 The Context for these Guidelines 8 Digital Education Action Plan 8 Artificial Intelligence and Data Use 10 EU Policy on Artificial Intelligence and Regulatory Framework Proposal  12 Common Misconceptions about AI 12 AI and Data Use Examples in Education 14 Ethical Considerations and Requirements Underpinning the Ethical Guidelines 18 Ethical Considerations 18 Key Requirements for Trustworthy AI 18 Guiding Questions for Educators  19 Guidance for Educators and School Leaders 22 Using the Guiding Questions 22 Planning for Effective Use of AI and Data in School 26 Raising Awareness and Community Engagement 27 Emerging Competences for Ethical use of AI and data 28 Glossary of AI and Data Terms 32 Further Information 38  6 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Foreword From the way we stay informed to the way we make decisions, artificial intelligence (AI) is becoming ubiquitous in our economy and society. Naturally, it has reached our schools as well. ΑΙ in education is no longer a distant future. It is already changing the way schools, universities and educators work and our children learn. It is making educational settings more responsive by helping teachers address each learner’s specific needs. It is fast becoming a staple in personalised tutoring and in assessment. And it is increasingly showing its potential to provide valuable insights in student development. The impact of AI on our education and training systems is undeniable, and will grow further in the future. Students and educators The Expert Group offered rich knowledge and expertise building on already benefit from AI the Ethics Guidelines for Trustworthy AI and The Assessment List for in their everyday lives, Trustworthy AI (ALTAI), areas that have been already high on EU’s in many cases without political agenda. Focusing on both the ethics of education and the b e i n g a w a r e of i t s ethics of AI and Data, the Group also took into account the proposed presence. Online learning legal framework for AI (Artificial Intelligence Act), the General Data environments often span Protection Regulation (GDPR), and the proposals for a Data Act and several continent s – for an EU Declaration on digital rights and principles. often without users being These Guidelines are to be used in schools across Europe and we shall entirely aware how and actively promote them through the Erasmus+ programme. Collectively where their data is used. or individually, teachers and school leaders will now have a solid This raises specific ethical basis to venture out and expand their use of these technologies in a challenges when using considerate, safe and ethical way. AI and processing large amounts of data in education. It goes without saying: we must ensure These Guidelines, along with their use on the ground, are fundamental that teachers and educators understand the potential AI and big data to our ongoing efforts to achieve the European Education Area, while can have in education – while being aware of the associated risks. supporting the work being carried out by EU Member States. The Guidelines are part of a longer journey, while the EU is negotiating It is for this reason that I am pleased to share with you the present and preparing for a comprehensive and effective regulatory framework Ethical Guidelines on the use of AI and data in teaching and learning for trustworthy AI, to be implemented across all sectors in the EU, for educators. The Guidelines will undoubtedly help our teachers including education. And our work does not stop here. As we move and educators reflect on how they can use AI and data in their daily forward, we will continue to develop a better understanding of how to practices – and empower them to act accordingly. apply these technologies, allowing educators to be even more inclusive I am grateful for the valuable contribution of the Expert Group and pragmatic, especially in primary and secondary education. set up by the European Commission to the preparation of these Guidelines. This group brought together a wide range of experts: from practitioners to researchers in AI, data, ethics and education, as well as representatives of various international organisations, such as UNICEF, UNESCO and OECD.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 7 Therefore, I would invite all European teachers and educators to My warmest thanks to the experts of this Group who made this take advantage of these guidelines, and to share their feedback happen. Your ideas and dedication come to life in the pages that on their practical application and experience, as this will support follow. Thank you our ongoing efforts regarding the digital transition in education. We shall also strongly benefit from the views and experience of our pupils, their families, and all stakeholders in the field of education about the use and impact of AI in their daily work and how to make it further beneficial while avoiding risks and negative effects to human rights and our fundamental EU values. Our joint work on AI and data in education shows a shared commitment to the education community, to our learners, to their development and Mariya Gabriel well-being. These Guidelines are an important starting point. It is now up to all of us to promote them and put them into practice. I am counting on you.  8 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) The Context for these Guidelines Digital Education Action Plan The Digital Education Action Plan (2021-2027) is the renewed European Union (EU) policy initiative to support the sustainable and effective adaptation of the education and training systems of EU Member States for the digital age. The Digital Education Action Plan: offers a long-term strategic vision for high-quality, inclusive and seeks stronger cooperation at the EU level on digital education accessible European digital education; and underscores the importance of working together across sectors to bring education into the digital age; addresses the challenges and opportunities of the COVID-19 presents opportunities, including improved quality and quantity pandemic, which has led to the unprecedented use of technology of teaching concerning digital technologies, support for the for education and training purposes; digitalisation of teaching methods and pedagogies and the provision of infrastructure required for inclusive and resilient remote learning.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 9 The Digital Education Plan puts forward two strategic priorities, each of which have a number of actions for the period 2021-2027: The Digital Education Action Plan (2021-2027) has two strategic priorities To foster high-perfoming digital education To enhance digital skills and competences 1 ecosystems, we need: 2 for the digital age: infrastructure, connectivity and digital equipment support the provisions of basic digital skills and competences from an early age: effective digital capacity planning and development, digital literacy, including management of information overload including effective end up-to-date organisational capabilities and recognising disinformation computing education digital-competent and confident educators and education & training staff good knowledge and understanding of data-tensive technologies, such as AI high-quality content, user friendly tools and secure boost advanced digital skills: enhancing the number of digital platforms, respecting privacy and ethical standards specialises and of girls and women in digital studies and careers Under Priority 1: Fostering the development of a high-performing digital education ecosystem, the Digital Education Action Plan outlines a set of actions to foster the development of a high- performing digital education ecosystem. This includes a specific action to develop ethical guidelines on the use of AI and data in education and training to be shared with educators and school leaders.  10 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Artificial Intelligence and Data Use What is Artificial Intelligence? Throughout Europe, learners and educators increasingly use Artificial The listed AI techniques and approaches are: Intelligence (AI) systems, sometimes without realising it. Search a) M  achine learning approaches, including supervised, unsupervised engines, smart assistants, chatbots, language translation, navigation and reinforcement learning, using a wide variety of methods apps, online videogames and many other applications use Artificial including deep learning; Intelligence in our everyday lives. AI systems rely on data, which is collected in different modalities (e.g. sound, images, text, posts, clicks) b) L ogic and knowledge-based approaches, including knowledge and all together form our digital traces. representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and AI has great potential to enhance education and training for learners, expert systems; educators and school leaders. AI systems are currently helping some educators to identify specific learning needs, providing learners with c) S  tatistical approaches, Bayesian estimation, search and personalised learning experiences, and helping some schools to make optimisation methods. better decisions, so that they can more effectively use the teaching resources available to them. As AI systems continue to evolve and data usage increases, it is of utmost importance to develop a better understanding of their impact on the world around us, particularly in education and training. Educators and school leaders need to have at least a basic knowledge of AI and data usage in order to be able to engage positively, critically, When we talk about AI systems, we are and ethically with this technology and to properly use it to exploit its referring to software in computers or machines full potential. that are programmed to perform tasks The definition of an Artificial Intelligence system (AI system) proposed in the draft AI Act is “software that is developed with one or more that usually require human intelligence, e.g. of the techniques and approaches (listed below) and can, for a learning or reasoning. Using data, certain AI given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the systems can be “trained” to make predictions, environments it interacts with”. provide recommendations or decisions, sometimes without any human involvement.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 11 What do we mean by AI and data use in Why do we need these guidelines? Education? The use of AI systems can potentially enhance teaching, learning and assessment, provide better learning outcomes and help schools to Schools typically process substantial amounts of educational operate more efficiently. However, if those same AI applications are data including personal information about students, parents, staff, not properly designed or used carelessly, this could lead to harmful management and suppliers. Data collected, used, and processed in consequences. Educators need to be aware and ask questions whether education is often referred to as “educational data”. These consist AI systems they are using are reliable, fair, safe and trustworthy and of data recorded in student information systems for example, that the management of educational data is secure, protects the educational achievements, parent names, assessment grades, privacy of individuals and is used for the common good. “Ethical AI” as well as micro-level data generated when digital tools are used. is used to indicate the development, deployment and use of AI that When students interact with digital devices, they generate digital ensures compliance with ethical norms, ethical principles and related traces such as mouse clicks, data on opened pages, the timing of core values. interaction events, or key presses. In the same way when using intelligent tutoring systems (ITS) in classrooms, learning mathematics or modern languages produce learning activity traces. All this data can be combined to capture each student’s online behaviour. This type of trace data (digital usage and learning activity traces) is often used for learning analytics (LA). Data in student information systems can be further used for resource and course planning and to predict These ethical guidelines on AI and data usage dropout and guidance. in teaching and learning are designed to help educators understand the potential that the applications of AI and data usage can have in education and to raise awareness of the Given the large amount of data needed to possible risks so that they are able to engage train AI systems, the automating nature positively, critically and ethically with AI of algorithms and the scalability in its systems and exploit their full potential. applications, the use of AI raises important questions in relation to personal data, data protection and privacy. Schools are required to ensure that any data that they process is stored confidentially and securely and need to have appropriate policies and procedures in place for the protection and ethical use of all personal data, in compliance with the General Data Protection Regulation (GDPR).  12 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) EU Policy on Artificial Intelligence and Regulatory Framework Proposal As part of its digital agenda, on the basis of the “Ethics Guidelines for Trustworthy AI” presented in 2019 by the High-Level Expert Group on AI (AI HLEG), the European Commission proposed in 2021 a comprehensive legal framework for AI (AI Act) laying down mandatory requirements for “high-risk” AI systems in several areas, including education and vocational training. Built on the EU regulatory and policy developments on AI and data, which include GDPR and the proposal for a Data Act, the present guidelines, taking into account the specific context of education and training, provide awareness and practical guidance for educators who are increasingly confronted with the use of AI in their teaching practice. To understand better the EU policy context on trustworthy AI, please refer to: the proposed Regulatory framework on Artificial Intelligence¹; the work of the AI HLEG which includes the Ethics Guidelines for Trustworthy AI and the Assessment List for Trustworthy AI (ALTAI)²; as well as to the EU Commission work in the area of Data³. Common Misconceptions about AI There are many assumptions and concerns about AI and its short AI has no role in education and long-term impacts on our education systems and on society in general. Here some of the most common misconceptions about the AI is already changing how we learn, work and live and education use of AI and data in the education context are addressed. is being impacted by this development. Everyone should be able to contribute to the development of AI and also benefit from it. By AI is too difficult to understand making ethical principles a key focus of the conversation about the role of AI in education, we can open the way for AI systems and Many people who don’t have a computer science background are put solutions to be developed and used in an ethical, trustworthy, fair off by jargon associated with AI and data systems. Even those who and inclusive way. do have the relevant background can struggle to fully understand how AI works, as it is a broad and complex domain. This is sometimes AI is not inclusive referred to as the ‘black box’ problem as it is difficult to understand the AI system’s inner workings. Artificial Intelligence is not a specific AI can result in new forms of inequalities or discrimination and thing but a collection of methods and techniques to build an AI exacerbate existing ones. However, if properly designed and used, system. Rather than trying to understand the full functionality of AI it can also offer opportunities to improve access and inclusion - in systems, it is more important that educators are aware of the basic everyday life, in work, and in education. There is also significant mechanisms and limitations of AI systems and how AI systems can potential for AI to provide educational resources for young people with be used to support teaching and learning in a safe and ethical way. disabilities and special needs. For example, AI-based solutions such These guidelines are designed to provide some basic questions one as real-time live captioning can assist those with impaired hearing, should ask when considering the use of an AI system and provide easy while audio description can make access easier and more effective to understand use scenarios from education as well as a glossary to for people with low levels of vision. help with the terminology that is used to describe these systems and what they do. 1 Regulatory framework on Artificial Intelligence. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai 2 High-level expert group on artificial intelligence. https://digital-strategy.ec.europa.eu/en/policies/expert-group-ai 3 Shaping Europe’s digital future: Data. https://digital-strategy.ec.europa.eu/en/policies/data  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 13 AI systems can’t be trusted AI will undermine the role of the teacher As AI systems become more powerful, they will increasingly Many teachers fear that as the use and impact of Artificial Intelligence supplement or replace specific tasks performed by people. This in education broadens in the future, these systems will diminish their could raise ethical and trust issues regarding the ability to make fair role or even replace them. Rather than replacing teachers, AI can decisions using AI, as well as protecting the data collected and used to support their work, enabling them to design learning experiences that support those decisions. The complexity of the legal area can be a real empower learners to be creative, to think, to solve real-world problems, challenge for educators. However, the proposed EU AI Act will help to to collaborate effectively, and provide learning experiences that AI ensure that certain AI systems classified as “high-risk” (in view of the systems on their own cannot do. Moreover, AI can automate repetitive risks that they may pose to the health, safety and fundamental rights administrative tasks allowing more time to be dedicated to the of individuals) are developed by providers according to mandatory learning environment. In this way the role of the teacher is likely to be requirements to mitigate such risks and ensure their reliability. augmented and evolve with the capabilities that new innovations for Education authorities and schools should therefore be able to verify AI in education will bring. However, this requires dilligent governance that AI systems comply with the AI regulatory framework and focus of the development and use of AI applications and focus on sustaining on the ethical use of AI and data to support educators and learners teacher agency. in teaching, learning and assessment, while also adhering to the applicable data protection regulations.  14 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) AI and Data Use Examples in Education The use of AI systems in classrooms across Europe is growing and AI is being used in different ways to support teaching, learning and assessment practices. AI has great potential to enhance teaching and learning practices and help schools improve the way they are organised and how they operate. However, evidence- based research on the impact of AI in education is still limited so it is important to maintain a critical and supervised attitude. Sometimes, AI systems can be used in different ways to support teaching or facilitate learning. When we talk about the types of AI systems that are used for teaching, learning, assessment and school administration, a common distinction is made between “student-facing,” “teacher-facing,” and “system-facing” AI systems. Here we provide four use-cases which are categorised as: Student Teaching – Using AI to teach students (student-facing); Student Supporting - Using AI to support student learning (student-facing); Teacher Supporting – Using AI to support the teacher (teacher-facing); System Supporting – Using AI to support diagnostic or system-wide planning (system-facing). The use cases described below provide some insight into how AI systems are being used by educators and learners to support the teaching, learning and assessment process. STUDENT TEACHING Using AI to teach students Intelligent tutoring The learner follows a step-by-step sequence of tasks and gets individualised instruction or system feedback without requiring intervention from the teacher. The learner follows a step-by-step sequence of tasks through conversation in natural Dialogue-based tutoring language. More advanced systems can automatically adapt to the level of engagement to systems keep the learner motivated and on task. AI-based learning apps are used in formal and non-formal education contexts. They Language learning support learning by providing access to language courses, dictionaries and provide real- applications time automated feedback on pronunciation, comprehension and fluency.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 15 STUDENT SUPPORTING Using AI to support student learning Exploratory learning Learners are offered multiple representations that help them identify their own routes to environments achieving the learning goals. Formative writing Learners are provided with regular automatic feedback on their writing/assignments. assessment Data on each learner’s work style and past performance is used to divide them into groups AI-supported with the same ability levels or suitable mix of abilities and talents. AI systems provide collaborative learning inputs/suggestions on how a group is working together by monitoring the level of interaction between group members. TEACHER SUPPORTING Using AI to support the teacher Summative writing AI is used to evaluate and grade learners’ written work automatically. AI and machine assessment, essay learning techniques identify features such as word usage, grammar and sentence structure scoring to grade and provide feedback. Key words in student forum posts trigger automatic feedback. Discussion analytics provide Student forum monitoring insights to students’ forum activity and can highlight students who may need help or are not participating as expected. AI agents or chatbots provide answers to commonly asked questions by learners with simple AI teaching assistants instruction and directions. Over time, the AI system is able to broaden the range of answers and options provided. Pedagogical resource AI recommendation engines are used to recommend specific learning activities or resources recommendation based on each student’s preferences, progress and needs. 16 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) SYSTEM SUPPORTING AI to support diagnostic or system-wide planning Schools gather student data which are analysed and used to plan how available resources Educational data mining can be best allocated for tasks like creating class groupings, assigning teachers, timetabling, for resource allocation and highlighting students who may require additional learning support. Using learning analytics, cognitive skills such as vocabulary, listening, spatial reasoning, Diagnosing learning problem-solving, and memory are measured and used to diagnose learning difficulties, difficulties including underlying issues that are hard for a teacher to pick up but might be detected early using AI systems. AI based guidance services provide ongoing prompts or choice to create pathways for future education. Users can form a competence profile including previous education and include Guidance services their own interests. From this data, combined with up-to-date course catalogue or study opportunity information, relevant study recommendations can be created using natural language processing.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 17 “Ethical guidelines on the use of AI and data in teaching and learning are an incremental process of continuous deliberation and learning.” Expert Group on AI and data in education and training  18 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Ethical Considerations and Requirements Underpinning the Ethical Guidelines Ethical Considerations In developing these guidelines, four key considerations have been identified that underpin the ethical use of AI and data in teaching, learning, and assessment. These are human agency, fairness, humanity, and justified choice. Human agency relates to an individual’s capability to become a Justified choice relates to the use of knowledge, facts, and data competent member of society. A person with agency can determine to justify necessary or appropriate collective choices by multiple their life choices and be responsible for their actions. Agency underpins stakeholders in the school environment. It requires transparency and widely used concepts such as autonomy, self-determination, is based on participatory and collaborative models of decision-making and responsibility. as well as explainability. Fairness relates to everyone being treated fairly in the social These ethical considerations are intrinsically valuable and worth organisation. Clear processes are required so that all users have striving for in education. They guide educators and school leaders equal access to opportunity. These include equity, inclusion, non- in their decisions about the use of AI systems in education. The discrimination, and fair distribution of rights and responsibilities. key ethical requirements introduced below can help ensure that AI systems used in education and training are trustworthy and address Humanity addresses consideration for the people, their identity, relevant concerns. integrity, and dignity. We need to consider the well-being, safety, social cohesion, meaningful contact, and respect that is necessary for a meaningful human connection. That connection implies, for example, that we approach people with respect of their intrinsic value and not as a data object or a means-to-an-end. It is at the essence of the human-centric approach to AI. Key Requirements for Trustworthy AI The AI Act proposed by the Commission will lay down legally binding important concerns, such as the risk of bias or error affecting requirements for AI systems considered as “high-risk” in view of their educational outcomes: intended purpose⁴. This will include certain AI systems used in the Human agency and oversight including fundamental rights, area of education and vocational training. When the AI Act becomes children’s rights, human agency, and human oversight. applicable, education institutions as users of AI systems will be able to rely on the trustworthiness of these “high-risk” AI systems based on Transparenc y including traceability, explainability and the accompanying certification ensured by the provider, while having communication. to comply with certain obligations. Diversity, non-discrimination, and fairness including Irrespective of whether the AI systems will fall under the scope of accessibility, universal design, the avoidance of unfair bias, and the legal framework, companies developing and providing AI systems stakeholder participation, which allows use regardless of age, gender, (system providers) are encouraged to implement and apply ethical abilities, or characteristics - with a particular focus for students with requirements for trustworthy AI to their design and development special needs. processes. At the same time, it is important that schools and Societal and environmental wellbeing including sustainability educators are aware of these and are able to formulate relevant and environmental friendliness, social impact, society, and democracy. questions in order to better reflect on them. Privacy and data governance including respect for privacy, quality The below requirements, which are based on the AI HLEG Ethics and integrity of data, and access to data. Guidelines for Trustworthy AI, are therefore recommendable for any AI system deployed and used in education. They address 4 The proposed requirements are related to risk management, the training and testing data of the AI system and data governance, provision of technical documentation, record- keeping, transparency and provision of information to users, human oversight, and robustness, accuracy and cybersecurity.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 19 Technical robustness and safety including resilience to attack, providers to adequately assess the impact, address the potential security and general safety, accuracy, reliability, and reproducibility. risks, and realise the benefits of an AI system deployed and used in education. As such they guide the development, deployment and use Accountability including auditability, minimisation and reporting of trustworthy AI systems. of negative impact, trade-offs, and redress. The considerations and requirements can help educators, school leaders and technology Guiding Questions for Educators When considering the use of an AI system, while it may not While the guiding questions offer orientation and aim to initiate be necessary to understand how the AI system works, it is reflection by educators in their professional practices, they cannot important that the school or educator is able to formulate replace a comprehensive legal or ethical assessment. The latter should some relevant questions and engage in a constructive dialogue be conducted on the basis of the Assessment List for Trustworthy AI with AI systems providers or with the responsible public bodies (ALTAI) as well as the future AI Act. Nevertheless, the questions will (such as market surveillance authorities, education ministries, help educators to deal better with a complex and highly innovative regional and local education authorities and school authorities). technology and develop awareness. The guiding questions below are based on the key requirements for trustworthy AI systems and serve the purpose of enabling a constructive dialogue on their ethical use in education and training. Some of them are focussed more on practical implementation issues and others on ethical considerations. 1 Human Agency and Oversight Is the teacher role clearly defined so as to ensure that there is a teacher in the loop while the AI system is being used? How does the AI system affect the didactical role of the teacher? Are the decisions that impact students conducted with teacher agency and is the teacher able to notice anomalies or possible discrimination? Are procedures in place for teachers to monitor and intervene, for example in situations where empathy is required when dealing with learners or parents? Is there a mechanism for learners to opt-out if concerns have not been adequately addressed? Are there monitoring systems in place to prevent overconfidence in or overreliance on the AI system? Do teachers and school leaders have all the training and information needed to effectively use the system and ensure it is safe and does not cause harms or violate rights of students? 2 Transparency Are teachers and school leaders aware of the AI methods and features being utilised by the system? Is it clear what aspects AI can take over and what not within the system? Do teachers and school leaders understand how specific assessment or personalisation algorithms work within the AI system? Are the system processes and outcomes focussed on the expected learning outcomes for the learners? How reliable are the predictions, assessments and classifications of the AI system in explaining and evaluating the relevance of its use? Are the instructions and information accessible and presented in a way that is clear both for teachers and learners? 20 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) 3 Diversity, non-Discrimination and Fairness Is the system accessible by everyone in the same way without any barriers? Does the system provide appropriate interaction modes for learners with disabilities or special education needs? Is the AI system designed to treat learners respectfully adapting to their individual needs? Is the user interface appropriate and accessible for the age level of the learners? Has the usability and user-experience been tested for the target age group? Are there procedures in place to ensure that AI use will not lead to discrimination or unfair behaviour for all users? Does the AI system documentation or its training process provide insight into potential bias in the data? Are procedures in place to detect and deal with bias or perceived inequalities that may arise? 4 Societal and Environmental Wellbeing How does the AI system affect the social and emotional wellbeing of learners and teachers? Does the AI system clearly signal that its social interaction is simulated and that it has no capacities of feeling or empathy? Are students or their parents involved in the decision to use the AI system and support it? Is data used to support teachers and school leaders to evaluate student wellbeing and if so, how is this being monitored? Does use of the system create any harm or fear for individuals or for society?  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 21 5 Privacy and Data Governance Are there mechanisms to ensure that sensitive data is kept anonymous? Are there procedures in place to limit access to the data only to those who need it? Is access to learner data protected and stored in a secure location and used only for the purposes for which the data was collected? Is there a mechanism to allow teachers and school leaders to flag issues related to privacy or data protection? Are learners and teachers informed about what happens with their data, how it is used and for what purposes? Is it possible to customise the privacy and data settings? Does the AI system comply with General Data Protection Regulation? 6 Technical Robustness and Safety Is there sufficient security in place to protect against data breaches? Is there a strategy to monitor and test if the AI system is meeting the goals, purposes and intended applications? Are the appropriate oversight mechanisms in place for data collection, storage, processing, minimisation and use? Is information available to assure learners and parents of the system’s technical robustness and safety? 7 Accountability Who is responsible for the ongoing monitoring of results produced by the AI system and how the results are being used to enhance teaching, learning and assessment? How is the effectiveness and impact of the AI system being evaluated and how does this evaluation consider key values of education? Who is responsible and accountable for final decisions made regarding the procurement and implementation of the AI system? Is there a Service Level Agreement in place, clearly outlining the support and maintenance services and steps to be taken to address reported problems?  22 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Guidance for Educators and School Leaders Artificial Intelligence could play a key role in enhancing teaching, learning and assessment practices for educators and learners. Whether it is at the system-wide, school or classroom level, it is important that careful consideration is given to the ethical use of AI and data systems. This should be done on a continuous basis and led by the school management. Here are a number of basic steps that educators and school leaders can take to review how AI and data is being, or can be used throughout the school, so that it leads to improved outcomes for all learners while being mindful of the ethical considerations. Using the Guiding Questions The guiding questions can be used in different ways when reviewing examples based on their relevance to the proposed AI solution in an AI system prior to it being set up in a school or while it is being used. response to a given objective. Notably, some of these school case The questions can be asked of the educators themselves, of those scenarios will become subject to the regulatory framework on AI and making the decision at management level, or of the system providers. the respective regulated AI systems will be subject to mandatory The questions can also inform discussion with learners, parents and requirements and obligations. the wider school community.These school case scenarios provide examples of how the guiding questions can inform how AI systems are used in an ethical and responsible way. While all the guiding questions can be considered for each case, three questions are highlighted as Using adaptive learning technologies to adapt to each learner’s ability A primary school is using an Intelligent The following guiding questions highlight areas that require attention: Tutoring System to automatically direct Are the system processes and outcomes focussed on the expected learning learners to resources specific to their outcomes for the learners? How reliable are the predictions, assessments learning needs. The AI based system uses and classifications of the AI system in explaining and evaluating the learner data to adapt problems to the relevance of its use? Transparency learner’s predicted knowledge levels. As well as providing constant feedback Does the system provide appropriate interaction modes for learners with to the learner, the system provides disabilities or special education needs? Is the AI system designed to treat real-time information on their progress learners respectfully adapting to their individual needs? on a teacher dashboard. Diversity, non-Discrimination and Fairness Are there monitoring systems in place to prevent overconfidence in or overreliance on the AI system?   Human agency and oversight  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 23 Using student dashboards to guide learners through their learning A post-primary school is considering the The following guiding questions highlight areas that require attention: use of a personalised online student Does the AI system clearly signal that its social interaction is simulated and dashboard which will provide feedback that it has no capacities of feeling or empathy? to learners and support the development Societal and environmental wellbeing of their self-regulation skills. Instead of focusing on what the learner has learned, Is access to learner data protected and stored in a secure location and used the visualisations provide the student with only for the purposes for which the data was collected? a view of how they are learning. Privacy and data governance Is there a Service Level Agreement in place, clearly outlining the Support and Maintenance Services and steps to be taken to address reported problems? Accountability Providing individualised interventions for special needs A school is considering how AI systems The following guiding questions highlight areas that require attention: can help reduce barriers for students with Are procedures in place for teachers to monitor and intervene, special educational needs. The school is for example in situations where empathy is required when dealing currently trialling an AI system to detect with learners or parents? Human agency and oversight student support demands early on and provide tailored instructional support. Is information available to assure learners and parents of the system’s By detecting patterns of corresponding technical robustness and safety? Technical robustness and safety characteristics from measures such as Is the teacher role clearly defined so as to ensure that there is a teacher in learning performance, standardised tests the loop while the AI system is being used? attention span or reading speed, the How does the AI system affect the didactical role of the teacher? system suggests probabilities of specific Human agency and oversight diagnoses and related recommendations for interventions. 24 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Scoring essays using automated tools A school is looking at how AI systems can The following guiding questions highlight areas that require attention: support the assessment of student written Are there procedures in place to ensure that AI use will not lead to assignments. A provider has recommended discrimination or unfair behaviour for all users? an automated essay scoring system which Diversity, non-discrimination and fairness uses large natural language models to assess various aspects of text with high Who is responsible for the ongoing monitoring of results produced by the AI accuracy. The system can be used to system and how the results are being used to enhance teaching, learning check student assignments, automatically and assessment? Accountability identify errors, and assign grades. The Do teachers and school leaders understand how specific assessment or system can also be used to generate personalisation algorithms work within the AI system? Transparency sample essays. Over time, the system can train large artificial neural networks with historical cases that contain various types of student mistakes to provide even more accurate grading. The system has a plagiarism detection option which can be used to automatically detect instances of plagiarism or copyright infringement in written work submitted by students.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 25 Managing student enrolment and resource planning A school uses the data collected when The following guiding questions highlight areas that require attention: students enrol to predict and better Who is responsible for the ongoing monitoring of results produced by the AI organise the number of students who system and how the results are being used to enhance teaching, learning will attend in the coming year. The AI and assessment? Accountability system is also used to assist with forward planning, resource allocation, class Are there mechanisms to ensure that sensitive data is kept anonymous? allocations and budgeting. Are there procedures in place to limit access to the data only to those who This has enabled the school to consider need it? Privacy and data governance more student attributes than before, How is the effectiveness and impact of the AI system being evaluated and for example, to increase gender parity how does this evaluation consider key values of education? Accountability and student diversity. The school is now considering using prior grades and other metrics like standardised tests to develop targets for their students to achieve and to support educators to predict student success on a per subject basis. Using chatbots to guide learners and parents through administrative tasks A school uses a chatbot virtual assistant on The following guiding questions highlight areas that require attention: its website to guide learners and parents Does the AI system clearly signal that its social interaction is simulated and through administrative tasks such as that it has no capacities of feeling or empathy? enrolment for courses, paying course fees Societal and environmental wellbeing or logging technical support issues. The system is also used to help students to find Is there a strategy to monitor and test if the AI system is meeting the goals, learning opportunities, provide feedback purposes and intended applications? on pronunciation or comprehension. The Technical robustness and safety virtual assistant is also used to support Is there a mechanism to allow teachers and school leaders to flag issues students with special educational needs related to privacy or data protection? through administrative tasks. Privacy and data governance  26 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Planning for Effective Use of AI and Data in School When considering the use of AI and data, it is important that the school prepares and puts in place a collaborative and reflective process of internal school review. This requires educators to examine how they can use AI systems to positively support their teaching and student learning. Predicting the consequences and the impact of the use of data and AI in education can be very difficult. Therefore, an incremental approach to the development and deployment of these technologies and their assessment is needed. The idea is to gradually introduce these tools into their contexts and to constantly monitor the societal effects that can emerge, leaving open the possibility to step back when unintended consequences occur. Ethical application of AI in education requires agency at the student, educator, school management and institutional level. Review current AI systems and data use Carry out a pilot of the AI system The questions provided in these guidelines can be used as the starting Before introducing new AI systems across the school, it can be useful point to inquire about the AI systems that are already in place, or as to trial the system with a particular learner cohort. It is important a basis for discussion if considering the future use of AI and data to have a clear vision of what the school wants to achieve with the within a school. When carrying out a review, it is useful to list what new technology so that an informed decision can be made involving data is being gathered by the school and clarify what purpose this students and their parents. Specific evaluation criteria are required serves. Schools should consider if there is less specific information so that an informed judgement can be made on the effectiveness of that could be gathered to achieve the same outcome. They should the AI system in terms of improvement of learning outcomes, value also consider how long the data will be needed for and how the school for money and ethical use. This will also highlight some of the key might be able to retain it for as little time as possible. The European questions that may need to be asked of the supplier before purchasing Union General Data Protection Regulation (GDPR) requires this kind the system. of analysis. Collaborate with the AI system provider Initiate policies and procedures It is important to maintain contact with the AI system provider prior Prior to implementing an AI system, school wide policies and to deployment and throughout the lifecycle of the AI system. Look for procedures need to be put in place to establish expectations and to clear technical documentation and seek clarification on any aspects provide guidance on how to consistently deal with issues when they that are unclear. A Service Level Agreement (SLA) should be agreed arise. These could include measures for: with the provider setting out the support and maintenance services and steps to be taken to address reported problems. Assurances ensuring public procurement of trustworthy and human- should be sought from the provider in terms of their adherence to centric AI; applicable legal obligations. The school should also consider future implementing human oversight; dependence on the provider if, for example, it seeks to change provider in the future, or move to a different AI system altogether. It ensuring that input data is relevant to the intended purpose of is also important that any human oversight measures identified by the AI system; the provider are implemented by the school while the AI system is the provision of appropriate staff training; being used. monitoring the operation of the AI system and taking corrective actions; and, Monitor the operation of the AI system and evaluate the risk complying with relevant GDPR obligations, including carrying out a data protection impact assessment. The use of the AI system should be monitored on an ongoing basis to evaluate the impact on learning, teaching, and assessment practices. This will provide direction regarding what is appropriate as well as At school level it will be important to decide how monitoring will be inappropriate or unacceptable behaviour and will help ensure that organised and carried out, who will be responsible for monitoring and people are treated fairly and equally. It is important that policies and how progress will be determined and reported. The evidence gathered, procedures are communicated to educators, learners, and parents so as a result of ongoing monitoring, should inform and influence that they understand what is expected of them. the future use of AI systems or the decision not to use them in particular circumstances.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 27 Raising Awareness and Community Engagement Discuss with colleagues Communicate with parents, learners and Collaboration between educators contributes to school improvement school community and student success. Educators often draw support from each other Involving parents and learners in discussions and decision making will and can delegate tasks in ways that help them collectively to be more lead to better understanding and trust in what the school is aiming to effective. Working collaboratively can help to make more informed achieve through the use of AI systems. Careful consideration needs decisions and helps ensure a more consistent approach to using AI to be given to explain what data is being collected, what is being and data systems across the school. done with the data, how and why it is being collected, and how this is protected. It will be important to share these explanations with Collaborate with other schools learners and parents and to provide opportunities for them to provide Collaboration between schools is an effective way to share their feedback and voice possible concerns. Learners, depending on experiences and best practices and learn how other schools have their age, might require different approaches in order to engage them implemented AI systems. This can also be useful in identifying and so that they can participate in informed decision making. dealing with reliable providers of AI and data systems that adhere to the key requirements for trustworthy AI. It is important that schools Keep up to date participate in supervised projects and experiments organised at As AI systems continue to evolve and data usage increases, regional, national, or European level through initiatives such as it is very important to develop a better understanding of their Erasmus+. These provide opportunities for educators and school impact on the world around us, including in education and leaders to participate collaboratively in a process of applied research training. Educators will need to continue to keep informed of new and inform future use and development of AI and data use in schools. innovations and development through participation in continuing professional learning and involvement in communities of practice. School leaders will need to provide opportunities for staff to upskill and continue to develop competences for ethical use of AI and data.  28 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Emerging Competences for Ethical use of AI and data Educators and school leaders play a central role in the successful context of the European Framework for the Digital Competence adoption of AI systems and in realising the potential benefits of digital of Educators (DigCompEdu) which provides a general reference data in education. Because of this, it is important that teachers and framework to support the development of educator-specific digital school leaders are aware of and appreciate the opportunities and competences in Europe. Here are some potential indicators of the challenges of employing AI systems and how they can enhance emerging educator and school leader competences for ethical use of teaching, learning and assessment practices. This will lead to the AI and data in teaching and learning. development of new digital competences to be considered in the Area 1: Professional Engagement Using digital technologies for communication, collaboration, and professional development Competence element Potential Indicators Takes an active part in continuous professional learning on AI and learning analytics and their ethical use. Is able to critically describe Able to give examples of AI systems and describe their relevance. positive and negative impacts of AI and data use in education Knows how the ethical impact of AI systems is assessed in the school. Knows how to initiate and promote strategies across the school and its wider community that promote ethical and responsible use of AI and data. Aware that AI algorithms work in ways that are usually not visible or easily understood by users. Able to interact and give feedback to the AI system to influence what it Understand the basics of AI and recommends next. learning analytics Aware that sensors used in many digital technologies and applications generate large amounts of data, including personal data, that can be used to train an AI system. Aware of EU AI ethics guidelines and self-assessment instruments.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 29 Area 2: Digital resources Sourcing, creating, and sharing digital resources Competence element Potential Indicators Aware of the various forms of personal data used in education and training. Aware of responsibilities in maintaining data security and privacy. Knows that the processing of personal data is subject to national and EU regulation including GDPR. Knows that processing of personal data usually cannot be based on user consent in compulsory education. Data governance Knows who has access to student data, how access is monitored, and how long data are retained. Knows that all EU citizens have the right to not be subject to fully automated decision making. Able to give examples of sensitive data, including biometric data. Able to weigh the benefits and risks before allowing third parties to process personal data especially when using AI systems. Knows that AI systems are subject to national and EU regulation (notably AI Act to be adopted). Able to explain the risk-based approach of the AI Act (to be adopted). Knows the high-risk AI use cases in education and the associated requirements AI governance under the AI Act (when adopted). Knows how to incorporate AI edited/manipulated digital content in one’s own work and how that work should be credited. Able to explain key principles of data quality in AI systems. 30 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Area 3: Teaching and Learning Managing and orchestrating the use of digital technologies in teaching and learning Competence element Potential Indicators Knows that AI systems implement designer’s understanding of what learning Models of learning is and how learning can be measured; can explain key pedagogic assumptions that underpin a given digital learning system. Knows how a given digital system addresses the different social objectives of Objectives of education education (qualification, socialisation, subjectification). Able to consider the AI system impact on teacher autonomy, professional Human agency development, and educational innovation. Considers the sources of unacceptable bias in data-driven AI. Considers risks related to emotional dependency and student self-image when Fairness using interactive AI systems and learning analytics. Able to consider the impact of AI and data use on the student community. Humanity Confident in discussing the ethical aspects of AI, and how they influence the way technology is used. Participates in the development Can explain how ethical principles and values are considered and negotiated in of learning practices that use AI co-design and co-creation of learning practices that use AI and data (linked to and data learning design). Area 4: Assessment Using digital technologies and strategies to enhance assessment Competence element Potential Indicators Personal differences Aware that students react in different ways to automated feedback. Considers the sources of unacceptable bias in AI systems and how it can Algorithmic bias be mitigated. Aware that AI systems assess student progress based on pre-defined domain- specific models of knowledge. Cognitive focus Aware that most AI systems do not assess collaboration, social competences, or creativity. New ways to misuse technology Aware of common ways to manipulate AI-based assessment.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 31 Area 5: Empowering Learners Using digital technologies to enhance inclusion, personalisation, and learners’ active engagement Competence element Potential Indicators Knows the different ways personalised learning systems can adapt their behaviour (content, learning path, pedagogical approach). Able to explain how a given system can benefit all students, independent of AI addressing learners’ diverse their cognitive, cultural, economic, or physical differences. learning needs Aware that digital learning systems treat different student groups differently. Able to consider impact on the development of student self-efficiency, self- image, mindset, and cognitive and affective self-regulation skills. Knows that AI and data use may benefit some learners more than others. Able to explain what evidence has been used to justify the deployment of a Justified choice given AI system in the classroom. Recognises the need for constant monitoring of the outcomes of AI use and to learn from unexpected outcomes. Area 6: Facilitating learners’ digital competence Enabling learners to creatively and responsibly use digital technologies for information, communication, content creation, wellbeing and problem-solving Competence element Potential Indicators Able to use AI projects and deployments to help students learn about ethics of AI and Learning Analytics ethics AI and data use in education and training.  32 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I) Glossary of AI and Data Terms The words associated with AI and data use might sound unfamiliar or strange. Here are the most common terms associated with AI and data use and explanation of how it can apply to education. The explanations provided here are written to be accessible to those involved in schools and should not be considered as full technical definitions. The Assessment List For Trustworthy Artificial Intelligence (ALTAI)⁵ and the Commission’s Glossary of human-centric Artificial Intelligence⁶..AI Term What it means How it can apply to education A process or set of rules to be followed in AI algorithms can uncover patterns in students’ calculations or other problem-solving operations, performance and can help teachers optimise their ALGORITHM especially by a computer. teaching strategies/methodologies to personalise learning and improve outcomes. AR is an interactive experience where real-world AR creates opportunities for teachers to help environments and objects are supplemented by students grasp abstract concepts through interaction computer-generated 3D models and animated and experimentation with virtual materials. sequences which are displayed as if they are in This interactive learning environment provides AUGMENTED a real-world environment. AR environments can opportunities to implement hands-on learning REALITY (AR) employ AI techniques. approaches that increase engagement and enhance the learning experience. The computer system performs a function that Schools and teachers can use software to perform normally requires human involvement. A system many repetitive and time-consuming tasks like that can perform tasks without needing continuous timetabling, attendance, and enrolment. Automating AUTOMATION human supervision is described as autonomous. such tasks can allow teachers to spend less time on routine tasks and more time with their students. 5 ALTAI. https://digital-strategy.ec.europa.eu/en/library/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment 6 Estevez-Almenzar, M., Fernández-Llorca, D., Gomez, E., Martinez-Plumed, F., Glossary of human-centred artificial intelligence, Publications Office of the European Union, Luxem- bourg, 2022  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 33.AI Term What it means How it can apply to education Bias is an inclination of prejudice towards or Assumptions made by AI algorithms, could amplify against a person, object, or position. Bias can existing biases embedded in current education arise in many ways in AI systems. For example, practices i.e., bias pertaining to gender, race, culture, in data-drive AI systems, such as those opportunity, or disability status. produced through machine learning, bias in data Bias can also arise due to online learning and collection and training can result in an AI system adaptation through interaction. It can also arise demonstrating bias. In logic-based AI, such as through personalisation whereby users are presented rule-based systems, bias can arise due to how with recommendations or information feeds that are a knowledge engineer might view the rules that tailored to the user’s tastes. apply in a particular setting. BIAS It does not necessarily relate to human bias or human-driven data collection. It can arise, for example, through the limited contexts in which a system is used, in which case there is no opportunity to generalise it to other contexts. Bias can be good or bad, intentional or unintentional. In certain cases, bias can result in discriminatory and/or unfair outcomes ( i.e. unfair bias). Datasets so large that they cannot be collected, Through big data analysis, educators can potentially stored and analysed using traditional data identify areas where students struggle or thrive, processing applications. Big data refers not only understand the individual needs of students, and BIG DATA to the volume of data but also to the capacity to develop strategies for personalised learning. search, aggregate, and cross-reference large data sets. A program that communicates with people through Chatbots can be virtual advisors for learners and in text or voice commands in a way that mimics the process adapt to their learning pace and so help human-to-human conversation. personalise their learning. Their interactions with CHATBOT students can also help identify subjects with which they need help. The analysis of a large volume of data to bring out Educational Data Mining (EDM) based systems can models, correlations and trends. use data mining, machine learning, and statistics to DATA MINING better understand learners and the settings in which they learn. A collection of related data points, usually with a Datasets in education are mainly provided and used uniform order and tags. to support new educational research, and in the DATASET sharing and application of existing research. A computer file containing a collection of School administration systems contain databases independent works, data or other materials of student information in including personal profiling DATABASE arranged in a systematic or methodical way and and learning attainment data. These are sometimes individually accessible by electronic or other means. linked timetabling, assessment and learning management systems. 34 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I).AI Term What it means How it can apply to education Deep learning techniques are part of machine Deep learning AI systems have the potential to learning methods and are based on artificial neural predict minute aspects of educational performance DEEP LEARNING networks. They are applied in different tasks, e.g., which can aid in the development of strategies for to recognize objects in images or words in speech. personalised learning. A network of interconnected physical objects— IoT connected devices can provide learners better (things) that are embedded with sensors, software, access to everything from learning materials to INTERNET OF and other technologies so that they can connect communication channels and provides teachers with THINGS (IoT) and exchange data with other devices and systems the ability to measure student learning progress in over the internet. real-time. Learning analytics involves the measurement, Learning management systems record data collection, analysis and reporting of data about on student interaction with course materials, learners and their contexts, for purposes of their interaction with teachers and other peers, LEARNING understanding and optimizing learning and the and how they perform on digital assessments. ANALYTICS environments in which it occurs. Schools can use analysis of this data to monitor student performance, predict overall performance and facilitate the provision of support through personalized feedback to each student. The ability of a computer system to learn, extract Machine learning is a form of personalised learning patterns and change in response to new data, that is used to give each student an individualised MACHINE without the help of a human being. educational experience. Learners are guided through LEARNING their own learning, can follow the pace they want, and make their own decisions about what to learn based on system prompts. The translation of text or voice data by an Machine translation tools are used in language algorithm in real-time and without any human teaching to help learners improve their MACHINE involvement. understanding and pronunciation and can enable TRANSLATION teachers to devote more time to the content and communicative aspects of a language.  A N D D ATA I N T E AC H I N G A N D L E A R N I N G F O R E D U C AT O R S 35.AI Term What it means How it can apply to education Metadata is information used to describe, Through the use of metadata teachers can source reference, contextualise or characterise a data file and evaluate teaching and learning resources more METADATA such as a web page, image, video, document, or easily so they have more choice in the material they file. It is data that describes data, but it isn't the choose for their learners. This can help to direct each data itself. student to content at their ability or readiness level. Natural language processing is a form of AI Virtual tutoring system can use speech recognition to NATURAL that helps computers read and respond by identify problems in a student’s reading ability and LANGUAGE simulating the human ability to understand can provide real-time, automatic feedback on how to PROCESSING (NLP) everyday language. improve as well as helping to match the student with reading material that’s best suited to them. A computer system that is designed as a collection A neural network can be trained to learn a new skill of units and nodes, inspired by biological neural or ability by using the repetition method of learning. NEURAL NETWORK neurons in animals, connected in a way to transmit signals. OCR is the conversion of images of text (typed, Optical character recognition can help students with handwritten, or printed) into machine-encoded text. literacy difficulties by allowing them to listen to text OPTICAL rather than read it. It can also create a searchable CHARACTER digital document which enables students to look up RECOGNITION (OCR) the definition of a word more easily, or to bookmark different parts of the text. Information relating to an identified or identifiable Schools accumulate substantial amounts of natural person, directly or indirectly, in particular by personal information about students, parents, reference to one or more elements specific to staff, management, and suppliers. Schools, as data PERSONAL DATA that person. controllers, are required to store data which they process confidentially and securely and need to have appropriate policies and procedures in place for the protection and proper use of all personal data. The use of statistical algorithms and machine Predictive analytics can provide insight into which PREDICTIVE learning techniques to make predictions about the students require additional support, not only based ANALYTICS future using current and historical data. on their current and historical performance, but their predicted future performance. Robotics is the design, construction, and operation Educational robotics and simulators allow students of robots that can help and assist humans with a to learn in different ways in science, technology, variety of tasks. engineering, and mathematics (STEM) subjects, ROBOTICS with the objective to facilitate students’ skills and attitudes for analysis and operation of robots. Such activities can include design, programming, application, or experimentation with robots. 36 E T H I C A L G U I D E L I N E S O N T H E U S E O F A R T I F I C I A L I N T E L L I G E N C E ( A I).AI Term What it means How it can apply to education This is a type of machine learning where structured Supervised Learning systems are defined by their datasets, with inputs and labels, are used to train use of labelled datasets to train algorithms to and develop an algorithm. classify data or predict outcomes accurately. They SUPERVISED can help teachers identify at-risk students and target LEARNING interventions. They can also improve the efficiency of teaching, assessments, and grading by helping to personalise learning. Text-to-speech is the generation of synthesised Text-to-speech technology allows learners to focus speech from text. The technology is used to on the content rather than on the mechanics of TEXT TO SPEECH communicate with users when reading a screen is reading, resulting in a better under