An Evidence-Based Approach to Employability Curricula and Transferable Skill Development: A Mixed Methods Study

Abstract

Artificial Intelligence in higher education (AIED) is becoming a more important research area with increasing developments and application of AI within the wider society. However, as yet AI based tools have not been widely adopted in higher education. As a result there is a lack of sound evidence available on the pedagogical impact of AI for learning and teaching. This conceptual paper thus seeks to bridge the gap and addresses the following question: is artificial intelligence really the new big thing that will revolutionise learning and teaching in higher education? Adopting the technological pedagogical content knowledge (TPACK) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical foundations, we argue that Artificial Intelligence (AI) technologies, at least in their current state of development, do not afford any real new advances for pedagogy in higher education. This is mainly because there does not seem to be valid evidence as to how the use of AI technologies and applications has helped students improve learning, and/or helped tutors make effective pedagogical changes. In addition, the pedagogical affordances of AI have not yet been clearly defined. The challenges that the higher education sector is currently experiencing relating to AI adoption are discussed at three hierarchical levels, namely national, institutional and personal levels. The paper ends with recommendations with regard to accelerating AI use in universities. This includes developing dedicated AI adoption strategies at the institutional level, updating the existing technology infrastructure and up-skilling academic tutors for AI.

Using leadership to leverage ChatGPT and artificial intelligence for undergraduate and postgraduate research supervision

“…In this paper, we discussed five key benefits of AI and LLM to research supervision and three limitations attached to their usage. These were not designed to be exhaustive, but rather to provide initial theoretical arguments to support scholarly research in this area (Rudolph et al, 2023), considering some argue of limited long-term viability of AI in higher education (O'Dea & O'Dea, 2023;Popenici, 2023). Despite some emerging studies on ChatGPT and similar, the focus has largely been restricted to early tests and practical uses that present great promise but potentially lack a strong theoretical grounding (at this stage, Neumann et al, 2023).…”

AI implications for vocational foreign language teaching and learning: new meaning

“…Despite the fact that current studies reveal multiple roles of AI, the common trends demonstrate a comparatively low level of digitalization in the educational sector [22]. Moreover, it's getting apparent that AI's role in education is more about its administrative functions and solving a range of management tasks [23][24][25][26] than pure academic goals of learning and teaching [27].…”

Learning along the Way

“…AI literacy can be thought of as an extension of traditional literacy skills and as "part of the modern individual's essential toolkit" (Farrelly and Baker 2023, 7). This literacy entails an awareness of gender and racial prejudice, a knowledge of copyright and licensing, and the ability to recognise and counteract AI "hallucinations," such as "deepfakes" and misinformation (O'Dea and O'Dea 2023). It is further suggested by O'Dea and O'Dea (2023) that students need two key skills as part of their critical AI digital literacy: fact checking and the ability to write creative and effective prompts.…”