Curriculum and assessment design


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.

Practitioner Notes

  1. AI technologies have been adopted more widely in industry, Higher education sector globally is lagging behind this trend.
  2. Even though the perceived benefits of AI in education have been reported repeatedly, the actual usage is low.
  3. The current adoption of AI in higher education is mainly seen in the following areas: automated learning and information support; automated essay scoring; student dropout prediction and personalised learning.
  4. AI has the potential to enhance learning and teaching in higher education, however, the barriers and challenges at the national, institutional and personal levels need to be dealt with promptly and appropriately.