Podcast
Questions and Answers
Match the types of bias in AI-assisted assessment with their descriptions:
Match the types of bias in AI-assisted assessment with their descriptions:
Statistical bias = Bias occurring due to the system being based on a biased dataset Algorithm bias = Bias introduced through poorly designed or implemented algorithms Human bias = Bias that happens when human reviewers are influenced by the results of an AI system Exclusion and marginalisation = Potential negative effects of AI if not implemented consciously
Match the potential uses of AI in universities with their descriptions:
Match the potential uses of AI in universities with their descriptions:
Tailoring lessons = Using AI to adapt lessons to each individual student Streamlining administrative processes = Using AI to make administrative tasks more efficient Insights into learner's habits and motivations = Using AI to gain a deeper understanding of each student's learning patterns
Match the concerns about AI-assisted assessment with their descriptions:
Match the concerns about AI-assisted assessment with their descriptions:
Potential for bias = Concern that algorithms may lead to unfair or inaccurate results Exacerbation of exclusion and marginalisation = Concern that AI may further isolate or discriminate against certain groups Faultline = An area of potential risk or conflict in the use of AI
Match the causes of bias in AI-assisted assessment with their descriptions:
Match the causes of bias in AI-assisted assessment with their descriptions:
Signup and view all the answers
Match the potential effects of AI-assisted assessment with their descriptions:
Match the potential effects of AI-assisted assessment with their descriptions:
Signup and view all the answers