Educating the Educators on Generative Artificial Intelligence in Higher Education
Bibliographic record
Abstract
In the current spring of Artificial Intelligence, the rapid development of Generative AI (GenAI) has initiated vivid discussions in higher education. Opportunities as well as challenges have been identified and to cope with this new situation there is a need for a large-scale teacher professional development. With basic skills about GenAI teachers could use the new technology as an extension of the existing technology enhanced teaching and learning. The aim of this paper is to present and discuss the project FAITH (Frontline Application of AI and Technology-enhanced Learning for Transforming Higher Education). FAITH is a higher education pedagogical development initiative for institutional development for teachers with good fundamental skills in traditional pedagogy. A project with the overall objective of increasing the staff understanding of AI and to develop new competencies in the field of GenAI and technology enhanced learning. The research question that guided this study was: "What are the perceived opportunities, challenges and expectations of involving GenAI in higher education?" The overall research strategy for the FAITH project is design-based research, which involves iterative and cumulative development processes. In the early iteration that this study was a part of has been carried out inspired by Collective Autoethnography where members of the steering group behind the FAITH project, and members of the project team have constituted the main focus group. Data were collected by structured interviews where two GenAI tools also have been interviewed. Findings show that the expectations are high, but that the FAITH ambition of institutional development is depending on teachers’ motivation for taking an active part in the project. Another challenge could be that many teachers see GenAI as something that threatens the current course design, and that a general ban of GenAI is the appropriate solution. One of, several identified opportunities, is that a general revision of syllabi and assessment in an adaptation for GenAI enhanced learning would improve the current course design.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".