What happens when we move beyond GenAI panic—and focus instead on helping students learn to use these tools ethically and effectively? This episode highlights Jennifer Krebsbach and Victoria Cross’s study of student learning in a conceptual statistics course across six years, including before GenAI, when GenAI was available but not encouraged, and when GenAI was intentionally integrated into assessment.
See our extended episode notes at https://www.centerforengagedlearning.org/navigating-the-moral-genai-panic/.
What happens when we move beyond GenAI panic—and focus instead on helping students learn to use these tools ethically and effectively?
This episode highlights Jennifer Krebsbach and Victoria Cross’s study of student learning in a conceptual statistics course across six years, including before GenAI, when GenAI was available but not encouraged, and when GenAI was intentionally integrated into assessment:
Krebsbach, Jennifer M., and Victoria L. Cross. 2026. “Navigating the Moral Panic: Encouraging Appropriate Use of GenAI in the Classroom Rather than Condemning Innovation as Disruption.” Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2026.2686727
This episode was hosted, edited, and produced by Jessie L. Moore, Director of the Center for Engaged Learning and Professor of Professional Writing & Rhetoric.
60-Second SoTL is produced by the Center for Engaged Learning at Elon University.
Music: “Cryptic” by AudioCoffee.
Image in show art by magnific.
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Jessie L. Moore:
How might we teach appropriate use of GenAI rather than condemn innovation as disruption? That’s the focus of this week’s 60-second SoTL from Elon University’s Center for Engaged Learning. I’m Jessie Moore.
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In “Navigating the Moral Panic: Encouraging Appropriate Use of GenAI in the Classroom Rather than Condemning Innovation as Disruption,” Jennifer Krebsbach and Victoria Cross compared assessments of student learning in a conceptual statistics course pre-GenAI, when GenAI was available but not encouraged, and when GenAI was intentionally integrated. Their article appears as an open-access article in Assessment & Evaluation in Higher Education.
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After recognizing that some students were using GenAI inappropriately in open-note, proctored course quizzes, Krebsbach and Cross piloted a new quiz format, with two parts. In the first part, student learning about course content knowledge was assessed in a closed-note, paper-based quiz, administered during TA-led discussion sections. After the discussion section, students had 36 hours to complete an open-resource applied quiz that included explicit instructions on how to use large language models (LLMs) appropriate for the task. The authors framed the applied quiz as an opportunity to summarize data in a context that reflects today’s workplaces, where multiple tools, including search engines and LLMs, are available. However, the instructional team also tested questions in ChatGPT to ensure students would need to critically engage with the outputs to answer quiz questions.
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Following an encouraging pilot, the team implemented the new quiz format course-wide and then used quiz data from eight iterations of the course across six years to examine the impact of GenAI on student learning. The data included pre-GenAI—and pre-COVID—courses, GenAI available—but not intentionally integrated—sections, and GenAI-integrated sections.
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Krebsbach and Cross include an extended analysis in their article, but in brief, average student performance on knowledge questions increased compared to the pre-GenAI baseline during the GenAI-available courses, but there also was more variability in student performance. However, student performance on application questions declined compared to pre-GenAI baseline levels, and again, there was more variability in student performance in the GenAI-available courses. The authors suggest that some—but not all—students were using GenAI, but that they were unable to use it effectively for applied problems. Additionally, they note that access to GenAI might not have been equitable for all students.
In the GenAI-integrated courses, student performance on both knowledge and applied questions approximated the pre-GenAI baseline, with much less variability of scores. In these courses, all students had access to relevant GenAI tools, training in ethical and effective use of them, and choice in whether to use GenAI among all available tools.
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The authors’ study illustrates how intentional integration of GenAI in course pedagogies can prepare students to choose from all available tools and use them ethically and effectively. To quote from the authors, “As we all navigate this disruptive technology, it is important to monitor our own hypotheses about student use of GenAI and student learning and to hold in mind the core of our learning objectives and teaching philosophies. Though a disruptive technology may initially seem overwhelming, we can draw strength from having successfully navigated previous disruptions and find ways to design authentic assessments of student learning under new circumstances.”
To learn more about this study, visit our show notes for a link to the open access article.
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Jessie L. Moore:
Join us for our next episode of 60-second SoTL from Elon University’s Center for Engaged Learning for another snapshot of recent scholarship of teaching and learning. Learn more about the Center at www.CenterForEngagedLearning.org.
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