The Impact of AI-Generated Feedback on Student Performance in Business Cases: Preliminary Findings
Bibliographic record
Abstract
Cases are a commonly used pedagogical tool in business education. A common challenge of teaching and learning with business cases is the provision of prompt and detailed feedback on students’ written assessments of a case. Generative artificial intelligence (AI) learning tools can, in theory, be trained to provide prompt and detailed feedback aligned with the teacher’s prescribed expectations. This paper investigates the use of a generative AI learning tool to enhance student performance by providing real-time and specific feedback on the mechanics of their written content (correctly meeting the specifications of the case submission) for the first two cases students completed in a marketing cases class. Preliminary results suggest that early interactions between students and the AI learning tool resulted in increases of both student performance and persistence in completing the assignments. Additionally, students’ perceptions about the value of having real-time feedback increasing their engagement during case written assignments demonstrated a statistically significant increase from expectations (in the pre-intervention survey) to realized (in the post-intervention survey). Building on this preliminary success, future research is intended to utilize the AI learning tool to provide feedback on the quality of the written analysis.
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.
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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".