"Is this a trap?": Student teachers' perceptions and adoption of GenAI in assessments in three teacher education courses

Synthesis: Zou, Hounsell, Huijser, and Tse (2026) report an explanatory sequential mixed-methods study of how student teachers interacted with generative AI (GenAI) in three teacher education courses at a research-intensive Hong Kong university, where instructors explicitly permitted its use in open-ended, reflective assessments. Drawing on an anonymous survey (N = 85), 11 Cantonese-language interviews, course materials, and 158 assignment declarations, the study found that only 37.6% of students chose to use GenAI at all — far below the 79–83% adoption reported in comparable UK and Australian studies — and that those who did used it mainly for proofreading and clarity checking rather than text generation. Through a socio-technical lens the authors locate the explanation not in technology but in sociocultural factors: assessment design, the perceived learning value of the task, self-confidence, and fear of being wrongly accused of plagiarism. Nine of 11 interviewees described the permissive course policy as a potential "trap", a suspicion seeded by inconsistent GenAI rules in other courses the students took. The authors conclude that consistent, program-level assessment policy, exemplars, and feedback matter more than brief course-level instructions.

Key Findings

Most students declined GenAI even when it was allowed: Only 37.6% (32 of 85 surveyed students) interacted with GenAI in their assessments, while 62.4% (53) chose not to — markedly lower than the 79% reported at King's College London (Gonsalves, 2024) and 83% in an Australian study (Matthews et al., 2024), challenging the assumption that students exploit GenAI when invigilation is absent.
Adoption diverged sharply by course and level: The undergraduate Course A saw 14 of 23 students adopt GenAI, whereas the two postgraduate diploma courses saw majorities declining (Course B: 29 of 39 not adopting; Course C: 15 of 23).
Proofreading dominated, text generation was rare: Among the 32 adopters, proofreading was the most cited purpose (14, 43.8%), followed by checking whether their writing was clear (11, 34.4%); only 6 students used GenAI to generate texts, and every adopter reported some benefit (8 said it helped a great deal, 14 to some extent, 7 a little).
Self-declaration under-reported use: Of 158 enrolled students, declarations submitted with assignments diverged from survey responses in every course — Course A 8 declared vs 14 surveyed, Course B 6 vs 10, Course C 12 vs 8 — suggesting students feared declared GenAI use would affect their grades while anonymous survey answers would not.
Working alone and plagiarism fear drove non-adoption: Non-adopters most often said they preferred to work by themselves (41, 77.4%) or were concerned about being accused of plagiarism (22, 41.5%); only 7 (13.2%) cited lacking the knowledge or skills to use GenAI.
The permissive policy was read as a "trap": Nine of 11 interviewees worried about plagiarism accusations, and the word "trap" recurred repeatedly — students suspected the allowance was a lure to catch those who could not resist, a suspicion shaped by inconsistent GenAI policies across other courses and by a one-month institutional ban in 2023.
Engagement was high and GenAI-neutral: Assessment engagement averaged 4.21 out of 5 (SD = 0.46), with no significant difference between adopters and non-adopters (U = 781.5, r = 0.07, p = 0.536), reinforcing that engagement hinged on assessment design, not tool use.
Student teachers saw little relevance to their own classrooms: Secondary-sector participants assumed closed-book school examinations would expose any GenAI misuse, and kindergarten teachers saw GenAI as useful for administration rather than teaching — with almost no transfer of their own assessment experiences into their future practice.

Study Design & Method

Explanatory sequential mixed-methods design (Creswell & Creswell, 2022) with a survey followed by semi-structured interviews to explain the quantitative patterns.
Three purposively sampled teacher education courses (one undergraduate, two postgraduate diploma) in pre- and in-service programs at a research-intensive Hong Kong university, delivered September–November 2024; all assessments were open-ended and reflective except a closed-book invigilated quiz in Course B.
Survey of N = 85 of 158 enrolled students (response rates: 100% Course A, 60% Course B, 32.9% Course C), covering assessment engagement (Evans & Zhu's scale, adapted), GenAI adoption in the highest-weighted assessment, and demographics, plus three open-ended questions.
11 one-on-one online interviews in Cantonese conducted by a research assistant unconnected to teaching, transcribed verbatim, and analyzed by thematic analysis (Braun & Clarke, 2022); three themes-plus concerned task design, valuing the learning opportunity, self-confidence, plagiarism fear, and teacher-role approaches.
Instructional and declaration artifacts: course outlines, in-class briefing slides, and all 158 students' self-declarations of GenAI use submitted with their assignments, analyzed by qualitative content analysis.
Context of policy: the institution allows four levels of GenAI adoption (no use; limited use with permission; use with acknowledgment; free use); all three courses operated at level two, but individual instructors set and communicated their own rules, and Hong Kong universities had briefly banned GenAI entirely in early 2023.
Analysis: descriptive statistics, Mann–Whitney U tests (non-parametric, chosen over t tests because data were not normally distributed), and iterative coding; findings rest on self-reported data, a single institution, and the low Course C response rate.

What this means for practice

Instructors. Do not assume a permissive GenAI policy communicates itself: nine of the 11 interviewees described the allowance as a potential "trap", and only 37.6 percent of surveyed students (32 of 85) used GenAI in their assessments at all.
Designers. Show worked examples of acceptable and unacceptable use and give timely feedback on declarations instead of restating rules, since declarations diverged from survey answers in every course (Course A 8 declared vs 14 surveyed; Course B 6 vs 10; Course C 12 vs 8).
Instructors. Anchor assessment tasks in reflective, experience-based professional problems: non-adopters most often preferred to work alone (41 of 53, 77.4 percent) or feared being accused of plagiarism (22, 41.5 percent), while only 7 (13.2 percent) said they lacked the knowledge or skills to use GenAI; build confidence and task value deliberately, since Scaffolding and feedback that strengthen competence and value are assessment-design levers, not adjuncts.
Administrators. Harmonize GenAI rules across a program, because student behavior tracked the wider institutional culture — including a one-month institutional ban in 2023 and inconsistent rules in other courses — rather than any single course brief; use teacher education assessments to grow future teachers' assessment literacy, since embedding GenAI in them gives first-hand experience of responsible use that participants otherwise showed little intention of carrying into their own school assessments.
Instructors. Measure actual uptake and treat non-use as a legitimate choice: assessment engagement averaged 4.21 out of 5 with no significant difference between adopters and non-adopters (U = 781.5, p = 0.536), so tool use is not what carried engagement.

Limitations

The study covers three purposively sampled courses at a single research-intensive Hong Kong university, with a survey of 85 of 158 enrolled students and response rates of 100 percent in Course A, 60 percent in Course B, and 32.9 percent in Course C.
Findings rest on students' self-reported data, and reported use did not match behavior: declarations submitted with assignments disagreed with anonymous survey responses in every course (Course A 8 declared vs 14 surveyed; Course B 6 vs 10; Course C 12 vs 8).
All three courses operated at the same permitted level of the institution's four-level GenAI policy, with individual instructors setting their own rules, so the "trap" perception is bound to a context that had briefly banned GenAI entirely in 2023.
The qualitative themes come from 11 Cantonese-language interviews at that one institution and cannot be generalized to policy settings elsewhere.

Connected Concepts

Connected Articles

It Takes a Village... Program-Wide Approaches to Redesigning Assessment in a Time of Generative Artificial Intelligence (GenAI) — Program-wide assessment redesign in response to GenAI
How university students work on assessment tasks with generative AI: matters of judgement — Student judgment work when assessing with GenAI
Addressing student non-compliance in AI use declarations: implications for academic integrity and assessment in higher — Student non-compliance with AI use declarations
"Should I Tell My Teacher?" Student AI Disclosure Practices, Stigma, and Self-Regulated Learning in Higher Education — Disclosure dilemmas in student GenAI use
Beyond Detection: Redesigning Authentic Assessment in an AI-Mediated World — Moving assessment beyond detection toward authenticity

Citation

Zou, T. X. P., Hounsell, D., Huijser, H., & Tse, R. T. L. (2026). "Is this a trap?": Student teachers' perceptions and adoption of GenAI in assessments in three teacher education courses. Australasian Journal of Educational Technology, 42(1), 1–17.

Metadata

Study design	Survey, Interviews, Thematic Analysis
Level	Higher Ed, Teacher Education
Intended audience	Faculty Developers, Instructors, Assessment Designers, Policymakers
Learning and instruction	Self-Efficacy, Sociocultural Learning, Student Engagement
Technologies and techniques	Generative AI
Assessment and measurement	Assessment
Research methods and evaluation	Mixed-Methods Research
Equity, ethics, and responsible use	AI Use and Disclosure Statements
Foundations of AI in education	Academic Integrity

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