Ethical Considerations in the Use of AI for Higher Education: A Comprehensive Guide
Bibliographic record
Abstract
Conversational AI refers to the use of artificial intelligence technology to enable machines to engage in human-like conversations, allowing for interactive and dynamic interactions with users. There are several tools that are developed by using AI and are widely used across various domains. In education, AI tools can offer several benefits, such as increased accessibility, personalized learning experiences, and improved engagement for students. However, potential downsides may include concerns about data privacy, accuracy of responses, and overreliance on technology without human interaction for holistic learning. This research paper aims to provide a comprehensive guide on the ethical aspects of using AI in higher education. Drawing on insights from twenty recent research papers in the field, this paper discusses the right direction and attitude towards AI, the potential benefits for students, and the risks of electronic plagiarism, black box theory, and diminished creativity. The paper also examines whether AI should be prohibited in higher education to mitigate the potential negative effects. This paper contributes to the ongoing conversation on the role of AI in education and provides a foundation for future research.
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 | 0.001 |
| 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 | 0.000 |
| 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".