Engineered Prompts in ChatGPT for Educational Assessment in Software Engineering and Computer Science
Bibliographic record
Abstract
AI Assess, a ChatGPT-based assessment system utilizing the ChatGPT platform by OpenAI, composed of four components, is proposed herein. The components are tested on the GPT model to determine to what extent they can grade various exam questions based on learning outcomes, generate relevant practice problems to improve content retention, identify student knowledge gaps, and provide instantaneous feedback to students. The assessment system has been explored using software engineering and computer science courses and is successful through testing and evaluation. AI Assess has demonstrated the ability to generate practice problems based on syllabus information and learning outcomes. The components have been shown to identify weak areas for students. Finally, it has been shown to provide different levels of feedback. The combined set of components, if incorporated into a complete software system and implemented in classrooms with proposed transparency mechanisms, has vast potential to reduce instructor workload, improve student understanding, and enhance the learning experience. The potential for GPT-powered chatbots in educational assessments is vast and must be embraced by the education sector.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".