This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities. Practitioner notes: What is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning. Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified. Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices. What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education. A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels. The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems. Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviours, and improve student learning through providing personalised feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students’ knowledge, attitudes and concerns about big data and learning analytics through four focus groups (N=41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, ‘Uninformed and Uncertain’, represents students’ lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; ‘Help or Hindrance to Learning’, ‘More than a Number’, and ‘Impeding Independence’; represented students’ perceptions of the likely impact of learning analytics on their learning. ‘Driving Inequality’ and ‘Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how ‘personal’ versus ‘collective’ purposes or principles can intersect with ‘uniform’ versus ‘autonomous’ activity. The findings highlight the need the need to engage students in the decision making process about learning analytics.

A rationale for using a simulated teaching environment to train pre-service teacher candidates is presented, followed by the key components of the simSchool dynamic simulator created to accomplish this task. Results of analyses of two sets of data, for the areas of pedagogical practices and teaching skills, are used to illustrate that changes in pre-service educators can be assessed as a direct outcome of activities completed within the simulated environment. Major outcomes to date indicate that teacher candidates gain a sense of instructional self-efficacy (confidence in their competence) more rapidly using the simulator, compared to traditional teacher preparation classes and related activities. This outcome is true for pre-service candidates working with simulated students spanning the normal range of personality attributes and sensory abilities, as well as pre-service teacher candidates working with simulated students with disabilities.