“The Being of Being Creative” in Assessment: Learning from the Creative and Performing Arts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it