The article starts with a discussion about the proliferation of AI in most industries and EdTech not being an exception with investment in the sector ‘reaching $1047 billion from 2008 to 2017 (Mou, 2019)’.
Research on AIED started in the 70s and they discuss the evolution of educational systems from Eliza in 64 t0 66 to SCHOLAR to MYCIN. The research has evolved from this ITS field to include other paradigms and with current advance of AIED continues to grow,
They feel other research have been limited and their ‘temporal multiple-journal bibliometric analysis is needed to piece together the evolution of AIEd research in the past two decades’
Their review was based on 425 articles published between 2000 and 2019 and these were fed into ‘Leximancer for in-depth text analysis’. They supplemented this with manual analysis of the ‘representativeness of topics illustrated in each concept map’.
Through this research they were able to map out the paradigm shifts over the past 20 years. They saw the growth of online courses, then the emergence of virtual reality then big data which led to student profiling and learning analytics.
This table mapping out the various definitions in literature over the 20 years was interesting to peruse as it demonstrated the shift in focus over the years.
No. | Authors | Definition |
1 | AI techniques can permit the intelligent tutoring systems itself to solve the problems which it sets for the user, in a human-like and appropriate way, and then reason about the solution process and make comments on it. | |
2 | Summarized AI in education context as intelligent tutoring system that helps to organize system knowledge and operational information to enhance operator performance and automatically determining exercise progression and remediation during a training session according to past student performance. | |
3 | The authors summarized AI as artificially intelligent tutors that construct responses in real-time using its own ability to understand the problem and assess student analyses. | |
4 | AI is defined as computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks. | |
5 | AI is defined as computing systems capable of engaging in human-like processes such as adapting, learning, synthesizing, correcting and using of various data required for processing complex tasks. |
They discuss AI in relation to the student in terms of adaptive learning, in relation to the tutor in terms of supporting admin tasks and institutional learning in terms of supporting areas like data analysis. These are the same across all literature reviewed so far.
They define Deep learning within education in the areas of adaptive learning, performance predictions and student retention.
They review Roll and Wylie (2016) and their discussion of 47 articles over 30 years which recommended further research both in the evolution in collaboration with stakeholders and revolution of AIED which embedded design.
They discuss writing by Hinojo-Lucena et al. (2019) who reviewed 132 papers and concluded AIED especially in Higher education was still sparse.
They reviewed an article by Bond et al. (2019) which explored 146 articles in EdTech journals published between 2007 and 2018 who concluded there had been insufficient ethical consideration of AIED.
They mentioned Chan and Zary (2019) who looked at 37 articles and concluded the importance of addressing technical difficulties ‘in order to accelerate adoption’
They felt that AIED research is challenging due to the multiplicity of research outlets and their method of ‘a multiple-journal analysis, covering the history of AIED would provide a better overview.
They found 11 themes have emerged over the past twenty-years (2000–2019). They include ‘AI computer-assisted instruction (AI CAI) system, VR, ITS, AR, educational games, predictive modelling, adaptive learning, assessment design, educational agents and teaching elevation’. These themes are consistent within the literature review covered so far.
These themes were compared concepts ‘and the top concepts related to AIEd in four ways: a) application context, such as pedagogical deployment of AIEd (e.g. intelligent tutoring systems, expert systems); b) targeted outcomes (e.g. predicting student performance; identification of learning styles); c) technologies being deployed (e.g. VR, mobile educational features); and d) learning environment (project-based learning environment)’.
The overall review indicates that the published research focuses on how technologies provide effective teaching and learning environments. This is interesting as it has been a focus on how Edtech aim to develop AIED tools and what the expectations of AIED would be, however the Council of Europe in its report does not believe the EdTech companies have effectively achieved this aim.
AI research focus which have come to the fore are ’the evolution of technology for instructional design, the integration of new technologies under a variety of teaching and learning contexts; and issues with implementing new systems and platforms, including appropriate technological and pedagogical adjustments’. This continues the theme of developing the AIED which is adaptive and relevant within contexts in which it is used and to the people who use them.
They then discuss the idea of building a ‘technology-organization-environment (TOE) framework. It is originally developed by Tornatzky and Fleischer (1990) to define innovation adoption within organisations looking at technological, organisational and environmental context arguing that their interaction would lead to enhanced teaching’.
Their review indicated that whilst developing the AIED environment was the focus of writings between 2000-2009, the focused changed to learning outcomes from 2010 to 2019.
Their research found the main paradigm shifts in literature on AIEd from distance learning in 2000-2009 including the use of VR for immersive experiences to personalized and adaptive learning with the arrival of big data from 2010 to 2019.
They identify the constraint of their research as having the possibility of missing what is currently happening in the field in terms of emerging themes due to lag time between research and publication.
They identify their search focuses mainly on the technological aspect of AIEd and ignores the ‘pedagogical, cultural, social, economic, ethical and psychological dimensions of education calling for future research to include these dimensions’. These are a vital aspect of any research into AIED as AIED does not take place in isolation but within various contexts and thus to effectively understand and respond to its impact and the paradigm shifts of AIED all of these must be considered moving forward.
This research has been invaluable in providing context and an overview of the focus of research in AIED over the past 20 years. The number of articles reviewed, over 400, ensures they have provided a broad-church context for their research and findings.
Tracing the paradigm shifts over the 20-year period they are able to identify current trends whilst calling attention to future research requirements particularly around pedagogical relevance and context.
By Nana Ofori-Atta Oguntola, (2024)
My course 'How to use AI in your creative practice' is available at www.famk.co.uk
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