Colloquial engagement theory with AI awareness (CET-AIA): A new creative pedagogical framework for ethical assessment in the age of artificial intelligence
Bibliographic record
Abstract
• Introduces CET-AIA, a new framework for ethical AI-resistant assessment. • Embeds colloquial, context-rich questioning to reduce AI-assisted cheating. • Empirical tests show students adapt while AI performance declines sharply. • CET-AIA enhances critical thinking, engagement, and assessment reliability. • Provides scalable, creative strategies for future AI-aware pedagogy. This study introduces the Colloquial Engagement Theory with AI Awareness (CET-AIA), a novel pedagogical framework developed to address academic integrity challenges in higher education posed by generative AI systems. CET-AIA integrates informal, context-sensitive questioning into assessment design to discourage AI-assisted cheating and enhance authentic student engagement. A quasi-experimental design was implemented across three undergraduate courses, comparing student and AI (ChatGPT-3.5) performance on traditional versus colloquial multiple-choice assessments. Theoretical foundations were drawn from Constructivism, Cognitive Load Theory, Sociocultural Theory, and Authentic Assessment. Performance trends were analyzed using t-tests, ANOVA, regression models, and mixed-effects modeling. Results indicate a significant initial decline in student scores under colloquial questioning, followed by gradual improvement, confirming both the cognitive challenge and adaptation process. In contrast, AI models showed a persistent performance drop when confronted with colloquial, context-specific questions. These outcomes demonstrate CET-AIA’s effectiveness in fostering deeper learning and in shielding assessments from AI exploitation. CET-AIA offers a scalable framework for designing AI-resistant assessments that promote critical thinking and real-world comprehension. The approach aligns with current educational priorities in fostering academic honesty and student-centered learning in digitally enhanced environments. This research is among the first to offer a theoretically grounded, empirically validated framework specifically designed to neutralize AI-assisted cheating through linguistic and contextual innovation. CET-AIA bridges the gap between AI ethics, pedagogy, and assessment, presenting a future-ready model for ethical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".