Simon Buckingham Shum

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first...

Comparing Generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness

Michael Henderson, Margaret Bearman, Jennifer Chung (2025)

Assessment & Evaluation in Higher Education

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

The rapid integration of Generative Artificial Intelligence (GenAI) into educational contexts has presented both opportunities and challenges for students seeking and using feedback. While AI-generated feedback can offer increased access, timely responses and personalised insights, concerns about the quality of AI-generated feedback still persist, including issues of bias, factual inaccuracies, and homogenisation. This study investigates how students use, value and trust AI-generated feedback compared to feedback from educators. This paper draws on a large-scale cross-sectional survey administered across four major Australian universities. A quantitative analysis of ...

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 (2022)

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

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...

Explainable Artificial Intelligence in education

Hassan Khosravi, Simon Buckingham Shum, Guanliang Chen (2022)

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

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...

Coauthorship integrity: Reconceptualising assessment validity for the age of generative artificial intelligence

Mohsen Ebrahimzadeh, Antonette Shibani, Simon Buckingham Shum (2026)

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

We consider how a future of pervasive human/AI coauthorship challenges current notions of validity and academic integrity. Specifically, we address widespread concerns that in non-proctored contexts, students are using generative artificial intelligence (GenAI) to submit texts they do not understand. Adopting an assessment validity lens, we show how GenAI undermines the integrity of multiple forms of validity evidence, leading us to propose Coauthorship Integrity as a new conceptual source of validity evidence for addressing these threats. Coauthorship Integrity is violated when students submit AI-generated content that they do not understand. To hold students accountable in this regard, a...

Building AI companions that prioritise learning over performance

Hassan Khosravi, Dragan Gašević, Shazia Sadiq (2026)

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

Generative AI, currently most visible in education through large language models (LLMs), is increasingly embedded in students’ everyday learning practices, supporting tasks such as writing, coding, reasoning, and analysis. Yet its educational value remains uncertain because systems designed to improve immediate task performance may also weaken the cognitive and metacognitive processes that support durable learning. This paper addresses this learning–performance paradox by asking how AI systems can be designed to support learning rather than merely produce better outputs. To address this, in this paper, we make three contributions. First, we synthesise current LLM-based study support with...