“The Being of Being Creative” in Assessment: Learning from the Creative and Performing Arts
Bibliographic record
Abstract
Assessment design shapes not only what students learn, but who they become as learners. In the era of generative artificial intelligence (GenAI), where information is abundant and recall is easily outsourced, higher education assessment must move beyond memorization and toward authentic tasks that cultivate deeper learning and ontological growth. This conceptual, reflective paper argues that assessment should be grounded in students’ mode of being, rather than restricted to knowing, having, or doing. Drawing on Barnett’s ontology of higher education, Biesta’s subjectification, and Su’s epistemological distinctions, this paper positions assessment as a formative site where agency, ownership, identity, and self-understanding can be intentionally developed. This paper draws on a narrative literature review that synthesises research on assessment in the creative and performing arts, selected purposively for its attention to creativity and learner empowerment. The synthesis identifies four quality indicators through which assessment engages students’ being: (1) shifting from reproduction to creation via open tasks and multimodal outputs; (2) situating assessment in naturalistic, public-facing contexts that connect learning to authentic audiences and communities; (3) adopting holistic approaches that value process, reflexivity, and becoming self-assessors; and (4) foregrounding communication through dialogue, critique, consultation, and the cultivation of an ontological student voice. The paper concludes that “assessment for becoming” is essential for meaningful engagement and integrity in AI-shaped learning environments.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".