Internationally, the interest in applying AI technologies in higher education is growing rapidly. Currently, the focus is on extensive research and implementation efforts through which AI applications are to be transferred into higher education contexts. Neglected aspects are the questions of how university agents can grasp the complexity of AI and how the exchange about AI can be strengthened. The chapter starts with this consideration and presents the scholarship of teaching and learning (SoTL) concept as a possible basis to strengthen the AI discourse between teachers and researchers. To this end, it first discusses different aspects of AI in higher education and then shows how AI projects can be structured. This is followed by an introduction to the SoTL concept. Subsequently, the concept will be examined as an example of the extent to which SoTL can be used to process AI projects.
TopIntroduction
In formal education, AI can have both positive and negative impacts on learning. As AI is now high on the policy agenda, it may appear that AI should be applied in as many educational settings as possible. When a new promising technology emerges, and when the limitations of technology and the challenges of applying it are often not perfectly understood, technology may seem to radically open new possibilities for solving old problems (Tuomi, 2018, p.28).
Versatile, efficient and evolving fast, Artificial Intelligence (AI) is considered a pioneering technology which will impact higher education forever (Aldosari, 2020, de Witt et al., 2020, Tuomi, 2018). Already, many AI-based systems – such as intelligent virtual assistants, chatbots or smart early warning systems meant to alert teachers to students at risk of dropping out – are being developed and tested by educational institutions all over the world (Zawacki-Richter et al., 2019, Fryer et al., 2019, Gallacher et al., 2018).
In higher education, the application of AI mainly focuses on personalized exercises, virtual mentoring systems, data-based feedback and the prediction of academic success – in short: on technology that supports students and teachers alike in creating a more conducive learning environment (Büching et al., 2019, Zawacki-Richter et al., 2019, de Witt et al., 2020).
At the same time, however, critics point out the drawbacks of the use of AI in higher education which might emerge: The risk of systematic discrimination against students, a lack of transparency and the possible invasion of privacy caused by a wrongful use of studentsʼ data cause concern even among supporters of the new technology (Büching et al., 2019, Kieslich et al., 2019, Alexander et al., 2019).
In order to create nuanced solutions for different applications of AI in higher education, an interdisciplinary discourse brings ethics, technology and educational studies together (Ocaña-Fernández et al., 2019) to create concepts such as Data Literacy (Ridsdale et al., 2015) or the so-called DELICATE Checklist (Drachsler & Greller, 2016) meant to help educational institutions create an environment in which AI-based technology can be used safely and with full disclosure concerning its use of student data.
There is, however, another view on the matter which is rarely taken into consideration: How can teachers join this discourse? Although the use of AI in educational settings will change their work in many ways and although their experiences might lead to altogether new applications of AI, professionals in higher education remain on the outskirts of the discussion. Encouraging them to contribute insights and ideas, therefore, require thinking about new ways to organize the discourse on AI in higher education.