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State-of-the-art reports published on Learning Analytics and Artificial Intelligence in Education

CIC contributes to new reports on the state of the art of Learning Analytics and Artificial Intelligence in Education

Australia is fortunate to have some of the leading researchers in Learning Analytics and Artificial Intelligence in Education,some of them being founders of these fields. Over the last year, CIC’s senior researchers Kirsty Kitto and Simon Buckingham Shum have been working with colleagues across the country on a series of research articles, documenting the state of play, and the road ahead. They’re now out!

Here’s the preview, click through to view the full articles…

Explainable Artificial Intelligence in Education

Hassan Khosravi, Simon Buckingham Shum, Guanliang Chen, Cristina Conati, Yi-Shan Tsai, Judy Kay, Simon Knight, Roberto Martinez-Maldonado, Shazia Sadiq, Dragan Gasevic

There are emerging concerns about the Fairness, Accountability, Transparency, and Ethics (FATE) of educational interventions supported by the use of Artificial Intelligence (AI) algorithms. One of the emerging methods for increasing trust in AI systems is to use eXplainable AI (XAI), which promotes the use of methods that produce transparent explanations and reasons for decisions AI systems make. Considering the existing literature on XAI, this paper argues that XAI in education has commonalities with the broader use of AI but also has distinctive needs. Accordingly, we first present a framework, referred to as XAI-ED, that considers six key aspects in relation to explainability for studying, designing and developing educational AI tools. These key aspects focus on the stakeholders, benefits, approaches for presenting explanations, widely used classes of AI models, human-centred designs of the AI interfaces and potential pitfalls of providing explanations within education. We then present four comprehensive case studies that illustrate the application of XAI-ED in four different educational AI tools. The paper concludes by discussing opportunities, challenges and future research needs for the effective incorporation of XAI in education.

Rethinking the Entwinement Between Artificial Intelligence and Human Learning: What Capabilities Do Learners Need for a World With AI?

Lina Markauskaite, Rebecca Marrone, Oleksandra Poquet, Simon Knight, Roberto Martinez-Maldonado, Sarah Howard, Jo Tondeur, Maarten De Laat, Simon Buckingham Shum, Dragan Gasevic, George Siemens

The proliferation of AI in many aspects of human life—from personal leisure, to collaborative professional work, to global policy decisions—poses a sharp question about how to prepare people for an interconnected, fast-changing world which is increasingly becoming saturated with technological devices and agentic machines. What kinds of capabilities do people need in a world infused with AI? How can we conceptualise these capabilities? How can we help learners develop them? How can we empirically study and assess their development? With this paper, we open the discussion by adopting a dialogical knowledge-making approach. Our team of 11 co-authors participated in an orchestrated written discussion. Engaging in a semi-independent and semi-joint written polylogue, we assembled a pool of ideas of what these capabilities are and how learners could be helped to develop them. Simultaneously, we discussed conceptual and methodological ideas that would enable us to test and refine our hypothetical views. In synthesising these ideas, we propose that there is a need to move beyond AI-centred views of capabilities and consider the ecology of technology, cognition, social interaction, and values.

Enhancing Learning by Open Learner Model (OLM) Driven Data Design

Judy Kay, Kathryn Bartimote, Kirsty Kitto, Bob Kummerfeld, Danny Liu, Peter Reimann

There is a huge and growing amount of data that is already captured in the many, diverse digital tools that support learning. Additionally, learning data is often inaccessible to teachers or served in a manner that fails to support or inform their teaching and design practice. We need systematic, learner-centred ways for teachers to design learning data that supports them. Drawing on decades of Artificial Intelligence in Education (AIED) research, we show how to make use of important AIED concepts: (1) learner models; (2) Open Learner Models (OLMs); (3) scrutability and (4) Ontologies. We show how these concepts can be used in the design of OLMs, interfaces that enable a learner to see and interact with an externalised representation of their learning progress. We extend this important work by demonstrating how OLMs can also drive a learner-centred design process of learning data. We draw on the work of Biggs on constructive alignment (Biggs, 1996, 1999, 2011), which has been so influential in education. Like Biggs, we propose a way for teachers to design the learning data in their subjects and we illustrate the approach with case studies. We show how teachers can use this approach today, essentially integrating the design of learning data along with the learning design for their subjects. We outline a research agenda for designing the collection of richer learning data. There are three core contributions of this paper. First, we present the terms OLM, learner model, scrutability and ontologies, as thinking tools for systematic design of learning data. Second, we show how to integrate this into the design and refinement of a subject. Finally, we present a research agenda for making this process both easier and more powerful.

Transitions through lifelong learning: Implications for learning analytics

Oleksandra Poquet, Kirsty Kitto, Jelena Jovanovic, Shane Dawson, George Siemens & Lina Markauskaite

The ability to develop new skills and competencies is a central concept of lifelong learning. Research to date has largely focused on the processes and support individuals require to engage in upskilling, re-learning or training. However, there has been limited attention examining the types of support that are necessary to assist a learner’s transition from “old” workplace contexts to “new”. Professionals often undergo significant restructuring of their knowledge, skills, and identities as they transition between career roles, industries, and sectors. Domains such as learning analytics (LA) have the potential to support learners as they use the analysis of fine-grained data collected from education technologies. However, we argue that to support transitions throughout lifelong learning, LA needs fundamentally new analytical and methodological approaches. To enable insights, research needs to capture and explain variability, dynamics, and causal interactions between different levels of individual development, at varying time scales. Scholarly conceptions of the context in which transitions occur are also required. Our interdisciplinary argument builds on the synthesis of literature about transitions in the range of disciplinary and thematic domains such as conceptual change, shifts between educational systems, and changing roles during life course. We highlight specific areas in research designs and current analytical methods that hinder insight into transformational changes during transitions. The paper concludes with starting points and frameworks that can advance research in this area.