Redefining assessment tasks to promote students’ creativity and integrity in the age of generative artificial intelligence
Bibliographic record
Abstract
Abstract The arrival of generative artificial intelligence (GenAI) has forced lecturers to adjust their assessment practices to ensure that students’ work is their own from a creative point of view, and free of plagiarism. This chapter proposes the Academic Integrity and Creativity in the Age of Artificial Intelligence (AICAI) model for the use of authentic assessment as a possible strategy to promote students’ creativity and integrity and thereby ensure the ownership of their written work. Lecturers are encouraged to rethink the assignments they design and examine each of the following components with an eye to integrity: their professional characteristics, the objectives for the assignment, the type of assessment that is appropriate for the needs of the student. Others include the cognitive offloading that will be done or not with GenAI, the type of authentic task they wish to propose and its characteristics, and the instructions and criteria that will be given to students. The choices made should engage students, thereby diminishing the temptation to plagiarize. By combining different strands of pedagogical theory and research, the AICAI assessment design model proposed in this paper has brought into focus the challenges as well as the opportunities that have emerged with the inclusion of GenAI in higher education. On a more practical level, it offers a systemic approach and advice as to how the challenges can be mitigated and benefits maximized for all parties involved in assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".