Academic Integrity and Final Exams in bCourses
Many instructors find it valuable to deliver a final exam or end-of-semester assignment in bCourses. Delivering an exam in bCourses makes it easy for students to access the exam content and convenient for the instructors to grade at the end of the term. Yet some instructors worry about safeguarding academic integrity within bCourses, particularly since students can access Generative AI tools while they’re taking exams within bCourses. While Berkeley does not currently support any solutions that would block student access to external websites while students are in bCourses, there are some effective practices that instructors can implement that may mitigate academic dishonesty while facilitating online and/or remote exams.
Clarify Expectations Before and During the Exam
Remind students of the Berkeley Honor Code.
Consider including a statement at the beginning of the exam asking students to acknowledge that they have read and understood the Honor Code. Some instructors invite students to write a brief integrity pledge (e.g., “I swear on my honor that I have neither given nor received aid on this exam.”). Research suggests that such reminders can reduce dishonest behavior in unproctored settings (see Zhao et. al 2023, Malesky et. al 2022, Tatum 2022).
Clarify your course or exam AI policy.
Remind students of your course policy regarding AI use during exams. For example, the Academic Senate’s “No AI” policy language notes that if unauthorized AI use is suspected, instructors may require a short in-person oral or written follow-up assessment, or report violations to the Center for Student Conduct. Clear, specific expectations help students understand what is and is not permitted.
Test assignments with Generative AI.
Before finalizing your exam, consider entering your prompts into a GenAI tool. This can help you identify questions that may be easily answered without meaningful engagement. Northern Michigan University offers guidance on this approach.
Design Assessments That Reduce Incentives to Cheat
Reduce competitive grading pressures.
Curved grading schemas can heighten student anxiety, particularly when students’ grades are directly compared. Research suggests that high-pressure competitive environments may increase the likelihood of academic dishonesty (Gallant and Rettinger 2024, Krou et. al 2020, Lang 2013). Clear criteria and transparent grading practices can reduce these pressures.
Emphasize reasoning and course-specific thinking.
Consider designing questions that require students to apply course-specific concepts, explain their reasoning, or connect ideas to particular class discussions, data sets, or case studies. Some instructors also explore open-book or open-note formats that prioritize analysis and decision-making over memorization. Additional examples are available on our page on Redesigning Assignments and Assessments to be Responsive to GenAI.
Consider open-resource formats.
Some instructors adopt open-book or open-note exams that emphasize synthesis and judgment. In such formats, the focus shifts from restricting tools to evaluating how students think.
Use bCourses Settings Strategically
bCourses allows instructors to set time limits for exams and adjust quiz settings such as “Show one question at a time” and “Lock questions after answering.” These settings can reduce opportunities to copy and circulate full exam content. When using time limits, remember that students with approved accommodations may require extended time (see our Accommodations in bCourses Quick Guide).
Some instructors choose to administer exams in person to allow for monitoring during completion. Others use tools such as Gradescope to manage online assignment or handwritten exams efficiently: instructors can scan paper-based assessments, grade them online, and return grades to bCourses.
No single technical approach can fully prevent the use of AI or access to outside resources. For this reason, many instructors combine thoughtful assessment design, clear communication of expectations, and appropriate delivery settings to support a culture of academic integrity.
References
- Gallant, T. B., & Rettinger, D. A. (2025). The Opposite of Cheating: Teaching for Integrity in the Age of AI. University of Oklahoma Press.
- Krou, M. R., Fong, C. J., & Hoff, M. A. (2021). Achievement Motivation and Academic Dishonesty: A Meta-Analytic Investigation. Educational Psychology Review, 33(2), 427–458. https://doi.org/10.1007/s10648-020-09557-7
- Lang, J. M. (2013). Cheating lessons. Harvard University Press.
- Malesky, A., Grist, C., Poovey, K., & Dennis, N. (2022). The effects of peer influence, honor codes, and personality traits on cheating behavior in a university setting. Ethics & Behavior, 32(1), 12-21.
- Tatum, H. E. (2022). Honor Codes and Academic Integrity: Three Decades of Research. Journal of College and Character, 23(1), 32–47. https://doi.org/10.1080/2194587X.2021.2017977
- Zhao, L., Peng, J., Yang, X., Yan, W., Ke, S., Dong, L. D., ... & Lee, K. (2023). Effects of honor code reminders on university students’ cheating in unproctored exams: A double-blind randomized controlled field study. Contemporary Educational Psychology, 75, 102213.