AECT – The Best Paper Award

By Donggil Song

 

I’m humbled that I’ve just received the award for The Best Paper – R.W. Buddy Burniske Award from International Division, Association for Educational Communications and Technology (AECT). November 2021.

 

The portion that I wrote for this paper is about Artificial Intelligence for Learning Sciences Research, and I quote: 

“Artificial Intelligence (AI) has been and will be widely integrated into education research (e.g., International Journal of Artificial Intelligence in Education, Journal of Educational Data Mining, and Journal of Learning Analytics), including learning performance profiling (Kizilcec et al. 2013; Vaessen et al. 2014), learning path and pattern profiling (Boroujeni and Dillenbourg 2019), learner performance prediction modeling (Mao et al. 2018), and learner retention prediction (Spoon et al. 2016). Educational big data and machine learning have been frequently addressed in the learning sciences field. Great promises on adaptive learning systems and personalized learning have been kept in the name of AI (see Colchester et al. 2017; Vandewaetere and Clarebout 2014); we may want to think about two different cases. First, AI-based research analysis might surpass researchers at subtle reasoning and judgment tasks. Second, AI-based learning support systems naturally and directly work with learners, and in some cases, surpass human tutors or instructors at instructional tasks. The first approach (the use of AI to indirectly support the learning process through educational data analysis) was named, “Back-end AI for human learning.” This approach has been used by researchers in the field of learning analytics and educational data mining. AI techniques and algorithms have been applied to investigate, examine, and analyze the learner’s learning process, behavior, and performance. On the other hand, AI has shown its potential to directly teach, instruct, facilitate, and support human learning. This approach can be named as “Front-end AI for human learning.” Traditional examples are answer-retrieval or information retrieval systems. From the informal reviews on the fields of practice and research, it seems that Korea is more focused on the front-end approach while the United States is leaning toward the back-end approach. AI learning support systems will be able to perform most of the tasks that currently have to be conducted by human instructors, teachers, tutors, coaches, trainers, and learners. Through the combination of both approaches explained above, I hope that AI systems will be capable of performing most of the personalized and individualized tasks that are traditionally and currently conducted by education practitioners.”

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