Is Artificial Intelligence Really the Next Big Thing in Learning and Teaching in Higher Education? A Conceptual Paper

Journal of University Teaching & Learning Practice Volume 20 Issue 5 Quarterly Issue 2 Article 05 2023 Is Artificial Intelligence Really the Next Big Thing in Learning and Teaching in Higher Education? A Conceptual Paper Xianghan (Christine) O'Dea Huddersfield University, United Kingdom, X.O' Mike O'Dea York St John University, United Kingdom, Follow this and additional works at: https://ro.uow.edu.au/jutlp Recommended Citation O'Dea, X., & O'Dea, M. (2023). Is Artificial Intelligence Really the Next Big Thing in Learning and Teaching in Higher Education? A Conceptual Paper. Journal of University Teaching & Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.05 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: Is Artificial Intelligence Really the Next Big Thing in Learning and Teaching in Higher Education? A Conceptual Paper 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 upskilling 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. Keywords artificial intelligence, big data, data analytics, pedagogical approaches, pedagogical affordances This article is available in Journal of University Teaching & Learning Practice: https://ro.uow.edu.au/jutlp/vol20/iss5/ 05 O'Dea and O'Dea: AI in higher education Introduction Artificial Intelligence or AI is used as an umbrella term for several related technologies including but not confined to, classical machine learning, deep learning, robotics and natural language processing (NLP). With the advent of ChatGPT, it has become popular in everyday media and educational settings (Eager & Brunton, 2023). AI techniques enable computers to learn and perform human-like cognitive tasks, such as predictions, and decision making through processing and analysing very large amounts of data (Holzinger et al., 2019; Awacki-Richter et al., 2019). AI techniques are closely integrated with big data and data analytics. In an education context, big data refers to students’ learning data and Data analytics is referred to as learning analytics, and is concerned with collecting, measuring and analysing students’ learning behaviours within different learning contexts (Clow, 2013). AI is now widely used in major industries, such as manufacturing, supply chain management, banking, and financial services. Not surprisingly, higher education sectors worldwide are attempting to follow the trend and aim to use AI technologies and tool to enhance learning and teaching. In fact, the two latest Horizon Reports (2022, 2023) have identified AI as one of the key technologies for postsecondary education and suggested potential applications of AI tools in learning and teaching in higher education. As discussed in the section below (affordances of AI), published studies, within the field of AIED have reported the implementation of different types of AI techniques (e.g., machine learning, natural language processing (NLP), automation and robotics) in higher education regarding providing automated information support to students, enabling tutors to auto-mark students’ assessments; and predicting student dropout. In recent months, large language models (LLMs) based AI text generators or generative chatbots, notably ChatGPT, have attracted a great deal of attention. These chatbots are considered to potentially disrupt higher education practice, as they are very user friendly and have ability to generate “human-like” responses to various questions, including relatively complex natural language queries (Crawford et al., 2023a; O’Dea & O’Dea, 2023). As a result, there have been ongoing debates in the higher education sector at the local, national and international level regularly about the potential impact of such tool on ethics and academic integrity regarding academic assessments. Academic Editors It appears that even though its perceived impact is high, the actual adoption of AI in higher education is relatively low (Celik et al., 2022). There is a lack of clear and convincing evidence on the pedagogical impact of AI for learning and teaching, in particular, in the areas of students’ learning performance and learning experience (Chen et al., 2022; Ilkka, 2018). This is partially because so far much of the emphasis of the application of AI into education has not been placed on direct and immediate learning and teaching activities, but rather on digital administrative management (Chandra & Suyanto, 2019; Klos et al., 2021) or administrative workload of academic Section: Educational Technology Editor in Chief: Dr Joseph Crawford Senior Editor: A/Prof Michael Cowling Publication Received: 13 March 2023 Revision: 26 March 2023 Accepted: 20 May 2023 (...truncated)