Challenges and opportunities for classroom-based formative assessment and AI: a perspective article
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
The integration of artificial intelligence (AI) into educational contexts may give rise to both positive and negative ramifications for teachers’ uses of formative assessment within their classrooms. Drawing on our diverse experiences as academics, researchers, psychometricians, teachers, and teacher educators specializing in formative assessment, we examine the pedagogical practices in which teachers provide feedback, facilitate peer- and self-assessments, and support students’ learning, and discuss how existing challenges to each of these may be affected by applications of AI. Firstly, we overview the challenges in the practice of formative assessment independently of the influence of AI. Moreover, based on the authors’ varied experience in formative assessment, we discuss the opportunities that AI brings to address the challenges in formative assessment as well as the new challenges introduced by the application of AI in formative assessment. Finally, we argue for the ongoing importance of self-regulated learning and a renewed emphasis on critical thinking for more effective implementation of formative assessment in this new AI-driven digital age.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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