Assessment validity is a deeper problem than AI-output detection, and disallowed use clusters with time pressure and unclear expectations¶

Claim · All claims
Evidence · 1 study (2 entries) · review r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In a 2026 FSE Companion study, students' descriptions of inappropriate or disallowed LLM use clustered around programming assignments, routine coursework, and documentation, with time pressure and unclear expectations playing an important role. → Qusay H. Mahmoud 2026 q2 i? Students and educators differ in how they interpret AI use and its assessment impact, and students encounter different rules across courses. → Qusay H. Mahmoud 2026 (2)

Evidence¶

Qusay H. Mahmoud 2026¶

Qusay H. Mahmoud. (2026). Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning. Preprint, September 2026. https://arxiv.org/abs/2609.29473 doi:10.48550/arxiv.2609.29473

q2 · i? · review · r2

The review reports Santos et al.'s 2026 FSE Companion study, finding disallowed-use descriptions clustered around routine coursework, "with time pressure and unclear expectations playing an important role", supporting integrity as partly a design problem.

"students’ descriptions of inappropriate or disallowed LLM use clustered around programming assignments, routine coursework, and documentation, with time pressure and unclear expectations playing an important role."

Qusay H. Mahmoud 2026 (2)¶

Qusay H. Mahmoud. (2026). Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning. Preprint, September 2026. https://arxiv.org/abs/2609.29473

q2 · i? · review · r2

The review cites cross-course and cross-national evidence that interpretations of AI use and rules vary across courses and between students and educators, making detection an unstable organizing strategy for assessment.

"Students and educators differ in how they interpret AI use and its impact on assessment [Zastudil et al., 2023, Kizilcec et al., 2024], and students encounter different rules across courses [Keuning et al., 2024, Yabaku and Ouhbi, 2024]."

Discussion¶

Related Claims¶

• Unclear policy, workload pressure, and lack of mechanisms to monitor student AI use undermined staff confidence and perceived fairness during AIAS implementation — related
• Existing GenAI assessment frameworks (traffic light, two-lane, AIAS) remain broad, lack discipline-specific applicability, and are unvalidated in authentic settings — related
• Evidence types differ systematically in probative value: detection outputs cluster at weak inferential force while admissions, observed exam behaviour and fabricated references carry the strongest profiles — related
• Nursing academics report limited, unclear and inconsistently applied institutional guidance on GenAI use in teaching and assessment — related
• Instructors frequently encounter suspected inappropriate AI use but lack confidence in their current investigation processes — related