Harnessing Large Language Models for Scalable and Effective Formative Assessment in Higher Education: A Review
Bibliographic record
Abstract
Formative assessment is an integral component of higher education, fostering student learning through feedback, reflection, and iterative improvement. However, despite its pedagogical importance, widespread adoption of formative assessment is often hindered by time constraints, resource limitations, and scalability challenges. The objective of this study is to examine how large language models (LLMs) offer a potential solution to support and enhance formative assessment in higher education across diverse educational contexts by enabling automated, personalized, and scalable feedback that is sustainable and accessible. In this review, we comprehensively examine cutting-edge research and applications of LLMs in various components of formative assessment, including feedback generation, student self-assessment, peer review, and instructor support within the context of higher education. We explore the opportunities LLMs present in enhancing learning outcomes associated with formative assessments and current research gaps while critically discussing the challenges in practical implementations of integrating LLM-driven formative assessments in real-world classrooms. By synthesizing current advancements, this review provides educators and researchers with insights into the transformative potential and responsible implementation of LLM-driven formative assessments in higher education.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".