Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160-1173.
https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.13337
Artificial intelligence (AI) is increasingly integrating into our society. University education needs to maintain its relevance in an AI‐mediated world, but the higher education sector is only beginning to engage deeply with the implications of AI within society. We define AI according to a relational epistemology, where, in the context of a particular interaction, a computational artefact provides a judgement about an optimal course of action and that this judgement cannot be traced. Therefore, by definition, AI must always act as a ‘black box’. Rather than seeking to explain ‘black boxes’, we argue that a pedagogy for an AI‐mediated world involves learning to work with opaque, partial and ambiguous situations, which reflect the entangled relationships between people and technologies. Such a pedagogy asks learners locate AI as socially bounded, where AI is always understood within the contexts of its use. We outline two particular approaches to achieve this: (a) orienting students to quality standards that surround AIs, what might be called the tacit and explicit ‘rules of the game’; and (b) providing meaningful interactions with AI systems.
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 1-13.
https://doi.org/10.1080/02602938.2024.2335321
Generative artificial intelligence (AI) has rapidly increased capacity for producing textual, visual and auditory outputs, yet there are ongoing concerns regarding the quality of those outputs. There is an urgent need to develop students’ evaluative judgement – the capability to judge the quality of work of self and others – in recognition of this new reality. In this conceptual paper, we describe the intersection between evaluative judgement and generative AI with a view to articulating how assessment practices can help students learn to work productively with generative AI. We propose three foci: (1) developing evaluative judgement of generative AI outputs; (2) developing evaluative judgement of generative AI processes; and (3) generative AI assessment of student evaluative judgements. We argue for developing students’ capabilities to identify and calibrate quality of work – uniquely human capabilities at a time of technological acceleration – through existing formative assessment strategies. These approaches circumvent and interrupt students’ uncritical usage of generative AI. The relationship between evaluative judgement and generative AI is more than just the application of human judgement to machine outputs. We have a collective responsibility, as educators and learners, to ensure that humans do not relinquish their roles as arbiters of quality.
Carless, D., & Young, S. (2023). Feedback seeking and student reflective feedback literacy: a sociocultural discourse analysis. Higher Education, 1-17.
https://doi.org/10.1007/s10734-023-01146-1
A significant and somewhat under-exploited aspect of feedback literacy research lies in students’ feedback-seeking behaviors. This research charts progress in oral feedback seeking by means of a three-year longitudinal inquiry focused on the feedback literacy development of an undergraduate co-author. The study is framed through sociocultural learning theories and the notion of feedback encounters to illustrate how social and relational feedback interactions encourage meaning-making and feedback uptake. Data comprise the student’s reflective journal of feedback experiences; regular documented interactions between the two co-researchers; digitally recorded and transcribed feedback encounters over four consecutive semesters; and teacher feedback on completed assignments. Sociocultural discourse analysis is deployed to uncover how the student and her teachers used talk to co-construct shared thinking about assignment work-in-progress. Features of the selected feedback encounters include interthinking and the interweaving of cumulative and exploratory talk. Reflecting on feedback-seeking experiences over time stimulated student feedback literacy development through progress in preparing for, participating in, following up, and working with emotions in feedback encounters. Originality and significance lie in drawing conceptual linkages between feedback seeking, reflection, and the development of student feedback literacy and in exemplifying innovative ways of conducting feedback research on the student experience of feedback. We introduce the concept of student reflective feedback literacy to represent considered analysis of feedback evidence and experiences informing ongoing efforts to seek, make sense of, and use feedback. Incorporating within the curriculum sustained opportunities for student reflections about feedback carries the potential to develop student feedback literacy and merits further investigation.
Lee, M., Chen, D. T., Tan, R. J. Y., & Hung, W. L. D. (2024). Re-conceptualising learner feedback agency: a situational, deliberative and entangled perspective. Assessment & Evaluation in Higher Education, 1-14.
https://doi.org/10.1080/02602938.2024.2318281
This article critically examines current conceptualisations of learner feedback agency (LFA) by juxtaposing them against debates and origins of agency to uncover their existing limitations. This examination found three limitations, portraying LFA as a static construct, overemphasising behavioural enactments over underlying deliberations, and presenting a unidirectional influence of structures on LFA. Responding to these limitations, the article proposes a three-fold re-conceptualisation of LFA as (1) situational, highlighting the adaptability of learners in dynamic learning environments, urging a shift from static to situation-specific considerations; (2) deliberative, underscoring the significance of examining learners’ thought processes beyond behavioural actions, and (3) entangled, recognising the co-constructive relationship between learners’ actions and existing structures, emphasising that learners not only respond to, but also influence structures in feedback. Implications of these perspectives are discussed, challenging prevailing assumptions and advocating for a more robust understanding of LFA.
Leenknecht, M. J., & Carless, D. (2023). Students’ feedback seeking behaviour in undegraduate education: A scoping review. Educational Research Review, 100549.
https://doi.org/10.1016/j.edurev.2023.100549
Feedback seeking in the organisational field has attracted sustained attention but seems relatively under-exploited in higher education. This scoping review aims to synthesize empirical research on feedback seeking in undergraduate education to develop a comprehensive understanding of students’ feedback seeking strategies and motivations, and related antecedents and outcomes. The method involved consultations with an expert panel, and a scoping review of 42 studies identified through rigorous search procedures. The key findings discuss learning enhancement, impression management and ego-based motives for feedback seeking; direct inquiry, indirect inquiry and monitoring strategies; and potential for feedback seeking outcomes to relate to high achievement. Broader implications focus on the interdependence between feedback seeking and feedback literacy; and the potential for cross-fertilisation of insights between research in organisations, medical education and broader higher education. Implications for practice focus on training and supporting student feedback seeking within psychologically safe learning environments.
Selwyn, N. (2024). Digital degrowth: Toward radically sustainable education technology. Learning, Media and Technology, 49(2), 186-199.
https://doi.org/10.1080/17439884.2022.2159978
This paper outlines how ideas of ‘degrowth’ might be used to reimagine sustainable forms of education technology. In essence, degrowth calls for a proactive renewal of technology use around goals of voluntary simplicity and slowing-down, community-based coproduction and sharing, alongside conscious minimalization of resource consumption. The paper considers how core degrowth principles of conviviality, commoning, autonomy and care have been used to develop various forms of ‘radically sustainable computing’. The paper then suggests four ways in which degrowth principles might frame future thinking around education technology in terms of: (i) curtailing current manipulative forms of education technology, (ii) bolstering existing convivial forms of education technology; (iii) stimulating the development of new convivial education technologies; and (iv) developing digital technologies to achieve the eventual de-schooling of society. It is concluded that mobilisation of these ideas might support a much-needed reorientation of digital technology in education along low-impact, equitable lines.
Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk tidsskrift for pedagogikk og kritikk, 10(1), 3-14.
https://researchmgt.monash.edu/ws/portalfiles/portal/572455663/568394011_oa.pdf
The recent hyperbole around artificial intelligence (AI) has impacted on our ability to properly consider the lasting educational implications of this technology. This paper outlines a number critical issues and concerns that need to feature more prominently in future educational discussions around AI. These include:(i) the limited ways in which educational processes and practices can be statistically modelled and calculated;(ii) the ways in which AI technologies risk perpetuating social harms for minoritized students;(iii) the losses incurred through reorganising education to be more ‘machine readable’; and (iv) the ecological and environmental costs of data-intensive and device-intensive forms of AI. The paper concludes with a call for slowing down and recalibrating current discussions around AI and education–paying more attention to issues of power, resistance and the possibility of re-imagining education AI along more equitable and educationally beneficial lines.
Taggart, G., & Zenor, J. (2022). Evaluation as a moral practice: The case of virtue ethics. Evaluation and Program Planning, 94, 102140.
https://doi.org/10.1016/j.evalprogplan.2022.102140
Evaluation is a moral practice. With this in mind, we look to moral philosophy and the theory of virtue ethics as a case study to identify instances where virtue ethics undergirds professional evaluation. To aid in this we also contrast virtue ethics with the more commonly discussed philosophies of consequentialism and deontology and their corresponding evaluation practices. An important question we hope to address is: why does it matter for evaluators to understand connections between moral philosophy and practice? We argue that a greater awareness of moral philosophy will aid evaluators in seeing both the plurality of moral considerations that undergird an evaluation practice, as well as aid in their ability to make judgements among these considerations. In particular, identifying the moral perspectives that ground evaluation practices will aid in evaluation flexibility and use by helping evaluators to tailor evaluations to the situations and moral issue at hand and bringing evaluations explicitly closer to their implicit moral roots.
Young, S., & Carless, D. (2024). Investigating variation in undergraduate students’ feedback seeking experiences: towards the integration of feedback seeking within the curriculum. Assessment & Evaluation in Higher Education, 1-13.
https://doi.org/10.1080/02602938.2024.2338537
Feedback seeking research envisages pro-active student roles in feedback processes but students seem to hesitate to seek feedback from their teachers despite the potential benefits it offers. Appreciating variation in students’ experiences of feedback seeking is crucial for understanding this issue. This phenomenographic interview-based research investigated variation in the experiences of 24 undergraduate students regarding feedback seeking. An outcome space of five categories was developed: (1) feedback seeking as unnecessary, (2) feedback seeking through monitoring, (3) feedback seeking as impression management, (4) feedback seeking for academic achievement and (5) feedback seeking for broader learning. Broader significance emerges through charting interplay between the mutually reinforcing concepts of feedback seeking and feedback literacy, suggesting benefits of enabling students to appreciate the value of feedback seeking when transitioning to higher education. Surfacing some of the negative views of feedback seeking expressed by students enables us to propose some teaching and learning approaches to reduce their concerns. These implications for practice include developing curriculum-wide opportunities for sustained feedback seeking; establishing psychologically safe environments for feedback seeking to flourish; and designing complex iterative assessments that encourage feedback seeking and uptake. Future possibilities for students to seek feedback from generative artificial intelligence are briefly sketched.
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Generative Age (powered by NYSCATE) explores the rapidly evolving world of generative artificial intelligence and its impact on education. Host Alana Winnick is joined by practitioners and thought leaders who are shaping the discourse around integrating generative AI into the classroom. Whether you are an educator, administrator, technologist, or simply interested in the future of education, join us on this journey through… The Generative Age.