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What 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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