Mark CarriganWhat do staff need to be ready for AI integration?
If we argue that AI ought to be incorporated into teaching and learning, it presents the obvious question of what ‘incorporate’ means in practice (which I discussed in this post) and what staff need to be able to do this competently. This latter question is one which Xue Zhou, Lei Fang and Lilian Schofie begin to answer in this paper. From pg 140:
For instructors to incorporate AI into their teaching successfully, they must develop skills across three primary knowledge domains: technological knowledge (TK), pedagogical knowledge (PK) and content knowledge (CK) (Celik, 2023). Within the field of AI literacy, TK encompasses an understanding of AI principles, tools and their practical applications, along with proficiency in using AI and educational technology tools. PK entails insights into the methodologies of teaching and learning, incorporating AI to bolster instructional techniques and the development of assessments, as well as in delivering educational content. CK involves expertise in the specific subject matter.
This is schematic and high level but it provides us with useful categories to think about the practical challenge. With regard to AI TK effectively equates to ‘AI literacy’ (not that this is necessarily a more concrete concept), PK relates to the deployment of that AI literacy in teaching and CK relates to subject knowledge for which AI is relevant and/or the role of AI in shaping their relationship to that subject knowledge. What I found valuable about their paper (note that I’m using the initial categories, not the later ones) are the empirical results about the limitations encountered in developing this knowledge.
From pg 149:
Our findings reveal that the inadequacy of AI training – focusing predominantly on technical aspects without addressing its social implications or integration into educational practices – contributes significantly to its low adoption rates. This approach to training fails to meet the comprehensive AI literacy standards recommended by Stolpe and Hallström (2024), which emphasise the need for technical skills, technological and scientific knowledge and socio-ethical understanding. Furthermore, the training does not sufficiently address key elements necessary for integrating AI into educational settings or the technology’s underlying principles.
From pg 149:
Our findings indicate that barriers to TK are primarily down to general unfamiliarity with AI tools or over-reliance on them. This is consistent with Gaber (2023), who explored the familiarity of academic staff with AI and found only a medium level of AI awareness. In TCK, which is based on knowledge about the technologies employed within the content field and on an understanding of how a particular technology may contribute to teachers’ content-specific knowledge (Koehler and Mishra, 2009), barriers identified include a lack of understanding of AI tools, uncertainty about which tools are most appropriate for specific teaching needs and concerns about the ethics of using these tools, as well as difficulties in integrating AI tools with content to enhance teaching
From pg 150:
PK encompasses knowledge about various technologies in relation to specific teaching approaches (Celik, 2023). The findings suggest that reluctance to adopt AI stems mainly from concerns about academic integrity and the possible decline in critical thinking skills, despite studies like that of Essien et al. (2024), which indicate that AI enhances critical thinking. There is an evident fear that students might become passive recipients of information, merely copying and pasting data provided by AI without engaging in rigorous fact-checking or evidence evaluation (Tlili et al., 2023). Furthermore, expressed concerns about student ‘laziness’ suggest a fear that AI could encourage a more lackadaisical approach to learning, where students rely too heavily on AI for answers.
What do I think we can learn from this? I would suggest these findings illustrate that training needs to be close to the context of delivery. Training about how to use a technology doesn’t address the questions of why, how, what for or when not to use it. It also needs to take the professional concerns underpinning a reluctance to engage in cultivating that knowledge seriously. Would it be possible to develop a university wide training programme adequate to those two challenges? I would suggest not at the level of content: you could cover the key bases but it would be abstract and general, with insufficient preparation for action because examples by nature would be broad. It would also miss the connection to context and values which are necessary to sustain engagement with knowledge across these three registers: the stakes would not meaningfully be there for participants.
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