• AI-Assisted Grading at Scale: Dr. Gerd Kortemeyer on the Future of Assessment

  • Nov 18 2024
  • Length: 47 mins
  • Podcast

AI-Assisted Grading at Scale: Dr. Gerd Kortemeyer on the Future of Assessment

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  • Summary

  • Artificial Intelligence, Leadership and the Future of Education

    Host:
    Richard Foster-Fletcher, Executive Chair, MKAI.org

    Guest:
    Dr. Gerd Kortemeyer, AI-Enhanced Learning Analytics Researcher, ETH Zurich and Michigan State University

    Guest Bio:
    Dr. Gerd Kortemeyer is a pioneering researcher in AI-enhanced learning analytics with experience at ETH Zurich and Michigan State University. He specializes in transforming complex data into actionable insights to support educators in real-time. His work bridges academic research with practical teaching applications, focusing on adaptive feedback systems that personalize learning experiences and improve student engagement. Dr. Kortemeyer’s research aims to elevate educational outcomes by integrating advanced analytics and technology-driven strategies into the classroom.

    Episode Title:
    AI-Assisted Grading at Scale: Dr. Gerd Kortemeyer on Efficiency, Student Feedback, and the Future of Assessment

    Episode Overview:
    In "AI-Assisted Grading at Scale," we explore the transformative impact of artificial intelligence on educational assessment with Dr. Gerd Kortemeyer. This episode delves into how AI technologies are reshaping grading, feedback, and assessment practices in further education, particularly in fields like mathematics and science, where grading tasks are highly repetitive. Dr. Kortemeyer provides a practical look at how AI can support educators with large-scale grading demands while highlighting the limitations AI faces in subjective areas. We discuss the evolving role of AI in classrooms, emphasizing why AI should be a supportive tool rather than a replacement for human judgment.

    Key Topics of Discussion:
    1. The practical benefits of AI in large-scale grading and assessment.
    2. AI's role in providing timely, consistent feedback to students.
    3. The limitations of AI in subjective assessments and the risks of over-reliance.
    4. AI as a supportive tool for educators, not a replacement.
    5. Challenges and considerations in using AI to personalize learning.
    Key 'Takeaway' Ideas:
    1. AI can improve the scalability and efficiency of grading in quantitative subjects but is less reliable for subjective assessments.
    2. While AI helps handle repetitive tasks, human judgment remains crucial for complex and nuanced feedback.
    3. Educators can use AI to enhance their teaching efforts, focusing on areas that require more personalized engagement.


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