South African Lecturers’ Views of ChatGPT: An AI Technology Used for Designing Online Assessments
Bibliographic record
Abstract
Even while Artificial Intelligence (AI) has long been a part of our lives, it has recently received more attention thanks to the introduction of ChatGPT, a Chat Generative Pre-Trained Transformer, since its launch in November 2022. The focus of this study is to investigate the potential of ChatGPT to assess student-teacher learning, which looks at its use for online assessments in South Africa. It emphasises South African lecturers’ views of ChatGPT, an AI technology used for designing online assessments. The expansion of online assessments has brought about various adaptable tools and techniques, and ChatGPT provides benefits, including real-time interaction and personalised responses. Nevertheless, problems such as prejudices and circumstantial limitations still exist. Notwithstanding this, ChatGPT does well at assessing critical thinking by examining evidence-based reasoning and logical reliability. When integrating ChatGPT, ethical deliberations such as algorithmic transparency, data security, and privacy are crucial. Ten participants participated in a qualitative study that examined ChatGPT's effects on online assessment and student-teacher relationships using the Community of Inquiry (CoI) model. By presenting lecturers with AI-driven techniques and promoting innovation and technology integration, participants highlight their impact in promoting professional development. As a cooperative tool, ChatGPT offers tailored feedback, detailed instructions, and culturally appropriate rubrics that encourage critical thinking and introspection. It is essential, however, to contextualise its application to combat biases and cultural twists within the African educational environment. This ensures that rather than replacing student-teachers' knowledge, AI supports them using inclusive and valuable assessments.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".