Generative AI in Higher Education: Guiding Principles for Teaching and Learning (Volume 1)
Bibliographic record
Abstract
The rise and promises of Artificial Intelligence in Education (AIED) has long been a topic of both excitement and skepticism (Chiu, 2023; Dwivedi et al., 2023; Farrokhnia et al., 2023; Hwang et al., 2020; Wang & Li, 2024). Higher education institutions exhibit different perspectives towards Generative AI (GenAI), with some institutions regarding it as a double-edged sword, a threat to academic integrity and thus outright prohibiting its application. Others, however, have actively incorporated it into academic practices as an innovative tool, developing ethical usage frameworks to ensure appropriate usage and integration. Nartey’s (2025) book Generative AI in Higher Education: Guiding Principles for Teaching and Learning aims to guide higher education institutions in embracing, accepting and implementing GenAI to transform the educational experience. It addresses key concerns about AI use in higher education, such as ethics, authenticity, equity, accessibility, and job impact. The author argues that these concerns should not hinder institutions from moving forward. Instead, they should guide the development of policies and guidelines that ensure AI’s benefits are realized without increasing existing inequalities or compromising the core mission of higher education: educating, inspiring, and preparing students to contribute meaningfully to society. The book provides guiding principles for using GenAI effectively and ethically to enhance teaching and learning without undermining academic integrity. It outlines a strategic roadmap for institutional implementation while critically addressing the complexities and ethical dilemmas inherent in adopting GenAI technologies within higher education contexts. This book clarifies that GenAI systems, like ChatGPT, are not inherently problematic; the central challenge lies in users' ethical engagement with them. It stresses that responsible interaction, not the technology itself, shapes societal outcomes. The book is divided into the following sections: an introduction, chapter one, chapter two, and chapter three.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".