Harnessing Large Language Models for Scalable and Effective Formative Assessment in Higher Education: A Review
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
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.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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