While Weller mentioned both pros and cons of applying Artificial Intelligence (AI) technology to education domain in the chapter 23, I tend to discuss the potential of AI in modern education from an alternative perspective, basing on the ideas of by Marguerite J. Dennis. She has been an expert of higher education administration for over 40 years and has written 6 books exploring the implication of AI in changing higher education in terms of admission, student progression and support. I will re
The reasons why I refer to her is her interpreting technology from a more pragmatic consideration (partly due to her experience in administration) pertaining to some overlooked problems of students. One point is how AI can improve educational support. Marguerite pointed out, from administrative perspective, that AI can be used in the student admission stage to predict academic impediment they might encounter during their first semesters, based on the algorithm and analytics of big data (e.g. personalised and frequent text messaging and communication) (Singh & Ritzhaupt, 2006). It may provide insights for educators to prepare educational intervention schemes in early stage (Dennis, 2018a). By the same token, the same approach also helps educators to follow students’ learning progress in real time rather than waiting for analysis at the end of each semester. Therefore, educators can craft supportive plans and take actions immediately to mitigate students problems (Dennis, 2018b).
Beside the academic aspect, the data analytics can also predict potential mental problems of students – it is another determining factor that is overlooked by many academics. This mental impediment is particularly important for international students, who are more likely to encounter conundrums such as homesickness and social isolation than their local counterparts. This is the very point that cannot be perceived by local researchers without empirical knowledge with education. Oftentimes, the academic difficulty for international student do not simply stem from professional reasons, but are attributed to mental factors. If such problems can be identified by data analytics with AI in admission stage, the intervention plan may be designed from a more holistic and effective perspective.
Despite the apprehension about the potential percussions of student intervention plan (as discussed in chapter 25 of Weller’s book), I would say that Marguerite’s ideas also deserve equal attention in favor of the judicious application of technologies AI to predict potential impediments of students from a early stage.
Dennis, M. J. (2018a). Artificial intelligence and recruitment, admission, progression, and retention. Enrollment Management Report TA – TT –, 22(9), 1–3. https://doi.org/10.1002/emt.30479 LK – https://royalroads.on.worldcat.org/oclc/7923992881
Dennis, M. J. (2018b). How will artificial intelligence change admissions? University World News. https://www.universityworldnews.com/post.php?story=20181024090311655
Singh, O., & Ritzhaupt, A. (2006). Student perspective of organizational uses of eportfolios in higher education. World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2006(1).