Undergraduate Science Students’ Perspectives on Inclusive and Ethical Generative AI in University Assessment

by Sarah Khalifa and Sarah Elaine Eaton

Introduction

In this blog post we report on a student-led project funded by the University of Calgary’s UREX Summer program, formerly known as the Program for Undergraduate Research Experience (PURE). Sarah Khalifa, a fourth-year Science undergraduate student, conducted the research supervised by Dr. Eaton.

Generative artificial intelligence (GenAI) is becoming increasingly common in university courses, including in science programs. As tools such as ChatGPT become part of students’ academic lives, instructors and universities are developing new rules and assessment practices to determine when and how these technologies should be used. In the GUIDE PURE project, we examined these changes from a perspective that can sometimes be missing from these conversations: undergraduate science students themselves. In our project we examined these changes from a perspective that can sometimes be missing from these conversations: undergraduate science students themselves.

This project builds on the GUIDE (Generative Understanding, Inclusive Design, and Ethical Assessment) Project, led by Dr. Sarah Elaine Eaton in the Werklund School of Education. The parent GUIDE Project examines how GenAI can be used in assessment while supporting inclusion and academic integrity. The GUIDE project focuses mainly on instructors, and the GUIDE PURE research centred specifically on undergraduate science students and their experiences with GenAI-related assessment practices.

The purpose of the GUIDE PURE study was to understand how undergraduate science students perceive GenAI-related assessment practices. In particular, we were interested in whether students consider these practices fair and inclusive, how clearly expectations around GenAI are communicated, and how GenAI policies may influence their learning and academic integrity.

Background

Science students are an important group to study because they regularly complete assessments such as high-stakes examinations, laboratory reports, and problem-solving assignments. Different forms of assessment may also create very different questions about appropriate GenAI use. Using AI to brainstorm an idea, for example, may raise different concerns from using it to complete an examination or generate an entire assignment.

Existing research already shows that students do not necessarily view all uses of GenAI in the same way. Engineering students in Bego et al. (2024), for instance, generally considered using ChatGPT for examinations or entire assignments unethical, while using it to support brainstorming and problem solving was viewed more positively. The study also identified a need for clearer guidance about acceptable and unacceptable uses of GenAI.

Research also suggests that experiences with GenAI can differ across academic disciplines. Qu et al. (2024) found differences in GenAI knowledge and engagement across fields, including lower engagement among students in pure sciences compared with some applied disciplines such as engineering.

In the GUIDE PURE project we addressed a specific gap by focusing directly on science students’ voices. Rather than assuming how institutional policies or assessment decisions affect students, this research asks them to describe those experiences in their own words. This student-centred perspective can help bridge the gap between university policy, teaching practice, and students’ actual experiences.

Theoretical Framing

Our study was underpinned by the six tenets of postplagiarism (Eaton, 2023):

• Hybrid human-AI writing will become normal
• Human creativity is enhanced
• AI can help overcome barriers (e.g., language barriers)
• Humans can relinquish control, but not responsibility
• Attribution remains important
• Definitions of plagiarism are likely to evolve

Following the University of Calgary’s #UHaveIntegrity campaign, we adopted a strengths-based approach to academic integrity. We regarded students as whole and complete human beings who bring their existing moral compass and ethical foundations to their academic work.

Research question (RQ)

The following research question (RQ) guided our study: How do undergraduate science students at the University of Calgary perceive the fairness, inclusivity, and academic integrity implications of GenAI-related assessment policies and practices in their courses?

Process and Methods

We used a qualitative research approach because we wanted to understand students’ experiences, reasoning, and perceptions in depth rather than simply measure how frequently they use GenAI.

We collected data through individual semi-structured interviews with undergraduate science students at the University of Calgary. Students were asked about their experiences with GenAI in coursework, including situations in which its use had been permitted, restricted, or discussed.

Fifteen undergraduate Science students at the University of Calgary participated in the study.

Semi-structured interviews were useful because they provided consistency across participants while still allowing students to explain their experiences in their own words. This was particularly important for a project concerned with fairness, inclusion, accessibility, and academic integrity, where students may interpret the same rule or assessment practice differently.

We used thematic coding to identify recurring ideas and patterns related to fairness, inclusion, accessibility, and integrity. The qualitative approach also reflects the student-centred nature of the project. One-on-one interviews created space for science students to discuss experiences that might be difficult to capture through a closed-ended survey, including concerns about accessibility, pressure, uncertainty, and academic integrity.

Findings and Results

At the time of writing, our analysis is ongoing. Our preliminary findings showed six common themes:

• Personal Ethical Dilemmas and Decisions
• Double Standards for Professors and Students
• Inconsistent or Insufficient Standards from Professors
• Tensions Between Those Who Use AI versus Those Who Don’t
• Students care how their learning is assessed
• Equity, Inclusion, and Accessibility

Our findings can be considered alongside previous research showing that students often distinguish between GenAI as a tool that supports their learning and GenAI as a tool that replaces their own work. They may also be navigating broader concerns about accuracy, privacy, fairness, accessibility, and institutional expectations. For example, Choi et al. (2025) found that students’ willingness and ability to evaluate AI-generated information depended partly on factors such as previous experience, trust, subject knowledge, and the ability to verify information.

The literature also suggests that institutional approaches matter. Davis (2024) argues that unclear or highly punitive GenAI policies may create additional barriers for some students and emphasizes clear guidance, equitable access, and inclusive approaches to academic integrity.

Conclusion

Sarah Khalifa’s reflection: The GUIDE PURE project has shown me the importance of including student voices in discussions about GenAI and university assessment. Universities are making decisions about GenAI at a time when the technology and students’ experiences with it continue to change rapidly. My project contributes to these conversations by asking science students directly how those decisions affect their learning, perceptions of fairness, inclusion, and academic integrity.

My PURE experience has also given me hands-on experience with multiple stages of qualitative research, including developing interview questions, conducting research ethically, working with qualitative data, analyzing findings, and communicating research to different audiences. These were key learning goals identified at the beginning of the project. The project also deepened my understanding of how technology can shape education and student experiences, particularly within science disciplines.

One limitation of my study was that we only collected data at the University of Calgary with a focus on only undergraduate Science students. If I were to conduct this research again, I would expand the participant pool. Future work could include students in other majors.

Ultimately, this research is not simply about whether students should or should not use GenAI. We set out to understand how universities can respond to an emerging technology in ways that protect academic integrity while also considering fairness, inclusion, accessibility, and student learning. By centering undergraduate science students’ experiences, this project can contribute practical information for designing assessments and policies that better reflect the realities students are navigating.

We will be working on more formal knowledge mobilization for this project once our analysis is complete.

References

Bego, C. R., Withorn, T., Danovitch, J., Thompson, A., Thomas, E., Gatsos, G. E., & Tran, A. (2024). Working towards GenAI literacy: Assessing first-year engineering students’ attitudes towards, trust in, and ethical opinions of ChatGPT. 2024 ASEE Annual Conference & Exposition. ASEE Conferences. https://doi.org/10.18260/1-2–48555

Choi, W., Bak, H., An, J., Zhang, Y., & Stvilia, B. (2025). College students’ credibility assessments of GenAI-generated information for academic tasks: An interview study. Journal of the Association for Information Science and Technology, 76(6), 867–883. https://doi.org/10.1002/asi.24978

Davis, M. (2024). Supporting inclusion in academic integrity in the age of GenAI. In S. Beckingham, J. Lawrence, S. Powell, & P. Hartley (Eds.), Using generative AI effectively in higher education: Sustainable and ethical practices for learning, teaching and assessment (pp. 21–30). Routledge. https://doi.org/10.4324/9781003482918-4

Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(1), 1–10. https://doi.org/10.1007/s40979-023-00144-1

Qu, Y., Tan, M. X. Y., & Wang, J. (2024). Disciplinary differences in undergraduate students’ engagement with generative artificial intelligence. Smart Learning Environments, 11, 51. https://doi.org/10.1186/s40561-024-00341-6

Author bios

Sarah Khalifa is a fourth year undergraduate Science student at the University of Calgary.

Sarah Elaine Eaton is a Professor in the Werklund School of Education and Director of the Postplagiarism Research Lab, University of Calgary.

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