[Air-L] CfP: BJET Special Section on Advancing Theory in the Age of Artificial Intelligence

[Vitomir Kovanovic]

BJET Special Section on Advancing Theory in the Age of Artificial Intelligence

Technology has been heralded as a needed innovation to improve educational practice.  Indeed, the use and sophistication of technologies to support multi-modal learning has advanced significantly in recent times, as have insights gained from learning analytics and data science. Technologies are now highly embedded in education and across all levels of schooling, ranging from early years to higher education and professional upskilling. Although there is an abundance of experimental and exploratory research investigating the use of these specific technologies, relatively few studies have leveraged technological advances to directly challenge or expand on learning and education theory. As such, our understanding of learning theory has remained relatively constant, despite the rapidly changing education context and almost ubiquitous access to, and availability of, technologies and information.  This can be seen through a review of the research literature in the fields of Education Technology, Learning Analytics (LA) or Artificial Intelligence (AI) in Education, where a plethora of works are focused on predictive models, testing of a novel technology or evaluations of impact. Comparatively, there are far fewer examples of research interrogating or posing new theories on learning experiences where instructors and designers integrate and complement the work of artificial agents.

The lack of critical engagement from research in education technology, and in particular LA and AI, with theory and challenging perspectives may stem from an over reliance on constrained data sets and traditional research methods (Perrotta and Selwyn, 2020; Poquet, et al., 2021). For instance, the majority of LA research findings tend to be derived from relatively small-scale, or single course studies (Dawson, et al., 2019). As an example in LA, Dawson and colleagues (2019) draw on Hevner's research maturity model to demonstrate that LA has stalled in a cycle of exploratory works. Although such exploratory research is critical for a field and brings much creativity and rapid trial and testing, there is also a need to progress works towards large scale replication studies and the establishment of new theory and revision of existing theories. However, the practicalities of undertaking AI in education and LA research is funnelled more towards the analysis of individual courses in lieu of multi-age and multidisciplinary data sets derived from traditional methodologies (Arocha, 2021; Dawson, et al., 2019; Jacobson et al, 2016). The theoretical framing of these works and interpretation of findings are often based on an education theory that was conceptualized in a markedly different era and learning context. Conventional learning theories were conceived for learning contexts with limited technology mediation, let alone the use of advanced technologies and intelligent agents to the extent that we are seeing today. These technologies have a significant influence on how courses are designed and delivered alongside instructional recommendations. More critically, they influence the concept of agency and autonomy as recommendations are often made without awareness of the unique developmental needs of each student and without regard to the longer term impacts of metacognitive skills being replaced by automated systems. Questions arise regarding the existing framing of learning theories and whether they remain applicable in an education system that provides for automation, recommendations, and intervention of learning misconceptions or for that matter establishing common standards and judgment of learning.

The lack of alignment between conventional theories and advanced technologies mediating new learning experiences points clearly to the need for a revision and postulation of new frameworks, views, theories that include these agents as members of the learning ecosystem.

In this special section, we invite publications that address, but are not limited to, one or more of the following topics:


  *   Posing a novel theory that is reflective of changing education dynamics. In particular, the modes of learning at the intersection of human and artificial cognition.
  *   Revising learning theory in consideration of advanced technologies.
  *   Presenting methodologies that seek to operationalise and test education and learning theory, including newer theories - such as Activity Theory, Complex Systems Modeling, Actor Network Theory, Connectivism, and Networked Learning.
  *   Advancing theoretical and methodological understandings to aid adoption and impact of educational technology.

Manuscripts should not normally exceed 6000 words, including references. All contributions should be prepared following the BJET Guidelines for Authors and submitted via the BJET manuscript submission system see:
https://onlinelibrary.wiley.com/page/journal/14678535/homepage/ForAuthors.html
All submissions will go through the usual process of blind peer-review. The editors will select papers for the special issue on the basis of their academic merit, quality and overall coverage of the theme of the special section.

Important Dates:
Full paper submission: 20th November 2022
Last Article Acceptances: 24th April 2023
Articles published online as soon as copyediting is completed.
Issue Publication: July 2023


Guest editors:
Shane Dawson, University of South Australia, Shane.Dawson at unisa.edu.au<mailto:Shane.Dawson at unisa.edu.au> (corresponding guest editor)
Caitlin Mills, University of Minnesota, cmills at umn.edu<mailto:cmills at umn.edu>
Srecko Joksimovic, University of South Australia, Srecko.Joksimovic at unisa.edu.au<mailto:Srecko.Joksimovic at unisa.edu.au>
Dragan Gasevic, Monash University, dragan.gasevic at monash.edu<mailto:dragan.gasevic at monash.edu>
George Siemens, University of South Australia, George.Siemens at unisa.edu.au<mailto:George.Siemens at unisa.edu.au>