Table 1.
Snapshot of percentage increase of research in the domain
| Nov 2018–Nov 2021 | Nov 2022–Nov 2023 | % Increase | |
|---|---|---|---|
| SCOPUS | 35 | 786 | 2,146% |
| ACM Digital Library | 8 | 197 | 2,363% |
| IEEE Xplore | 4 | 89 | 2,125% |
Figure 1.
GenAI prompt displaying user interaction with an LLM. GenAI, generative artificial intelligence; LLM, large language model.
Figure 2.
DIT (adapted from Rogers, 1995). DIT, diffusion of innovation theory.
Table 2.
Breakdown of participants' years of lecturing experience
| Years of experience | Number of participants |
|---|---|
| <1 year | 1 |
| 1–3 years | 2 |
| 4–7 years | 4 |
| 8–12 years | 8 |
| Over 12 years | 9 |
Table 3.
Components and dimensions within DIT framework
| DIT | ||
|---|---|---|
| Dimension | Adoption process component | Factors influencing adoption |
| Knowledge | Relative advantage | |
| Persuasion | Compatibility | |
| Decision | Complexity | |
| Implementation | Trialability | |
| Confirmation | Observability |
Graph 1.
Challenge of integration of GenAI into existing academic practice. GenAI, generative artificial intelligence.
Graph 2.
Rate overall advantage of using GenAI over traditional methods GenAI, generative artificial intelligence.
Graph 3.
Use of GenAI for teaching. GenAI, generative artificial intelligence.
Graph 4.
Use of GenAI for learning. GenAI, generative artificial intelligence.
Graph 5.
Use of GenAI for assessment. GenAI, generative artificial intelligence.
Graph 6.
How GenAI is currently used by computer science lecturers in an Irish TU. GenAI, generative artificial intelligence; TU, technological university.
Graph 7.
How easy can you observe outcomes which result from the use of GenAI? GenAI, generative artificial intelligence.
Graph 8.
How GenAI is currently used by computer science lecturers in an Irish TU. GenAI, generative artificial intelligence; TU, technological university.