Conference Agenda

The rapid expansion of generative artificial intelligence (AI) in higher education is reshaping how students learn, create, and engage with academic tasks, prompting new opportunities and challenges for higher education (Dwivedi et al., 2023; Meyer et al., 2023). Tools such as ChatGPT are increasingly embedded in students’ study routines, supporting drafting, revision, ideation, and concept development (Balabdaoui et al., 2023; Peters et al., 2023). This study examines an instructional design in which undergraduate business students used ChatGPT to obtain formative feedback on team-based blog assignments in an Introduction to Organisational Behavior course. By analysing 19 complete team submissions—including drafts, ChatGPT feedback, revised blogs, reflections, and instructor comments—the study explores how students engaged with AI feedback, the types of revisions they implemented, and the learning processes evident in their interactions.

The results indicate that ChatGPT consistently produced structured feedback aligned with assignment criteria, offering suggestions related to clarity, argument flow, scenario development, visual presentation, and surface-level writing quality (Kasneci et al., 2023a; Zhai, 2022). When students used the instructor-provided prompt, the feedback more often addressed theoretical accuracy, relevance of examples, argument strength, and conceptual alignment with organisational behaviour frameworks. This aligns with research showing that prompt design strongly shapes the quality and specificity of AI-generated feedback (Eager & Brunton, 2023; Wang & Li, 2024). In contrast, self-generated prompts produced more superficial responses focused on grammar, tone, and readability.

Despite receiving conceptual feedback, most teams primarily made surface-level revisions such as adjusting formatting, reorganising paragraphs, refining headings, and adding images—changes that improve readability but do not deepen theoretical insight. Students seldom incorporated suggestions requiring conceptual effort, such as elaborating on organisational behaviour theories, strengthening scenario-theory alignment, or clarifying causal logic. This pattern reflects broader concerns that students often use AI for low-effort tasks rather than deeper reasoning (Balabdaoui et al., 2023; Kasneci et al., 2023b).

A notable finding is students’ limited use of iterative prompting. Although ChatGPT’s affordances are maximised through iterative engagement (Kasneci et al., 2023a; Peters et al., 2023), teams rarely asked follow-up questions. When they did, these queries revolved around grammar checks rather than probing theoretical understanding or reasoning processes. This suggests gaps in both AI literacy and feedback literacy—skills essential for interpreting and applying feedback effectively (Carless & Boud, 2018; Pinto et al., 2023).

Student reflections further revealed limited metacognitive depth. Most reflections described superficial changes, often in checklist form, with minimal explanation of the learning value of revisions. Few reflected on theory comprehension, argument structure, or conceptual refinement. This echoes research showing that when students lack feedback literacy, they struggle to engage critically with comments—whether from instructors or AI tools (Cotton et al., 2023; Obenza et al., 2023). Instructor comments reinforced these gaps: instructors frequently highlighted conceptual weaknesses that ChatGPT had also identified but students had not addressed.

Taken together, the findings reveal both the promise and limitations of AI-mediated learning. AI supports improved clarity, structure, and visual design—key aspects of communication in management education (Peters et al., 2023). However, deeper conceptual learning depends heavily on student prompting skills, evaluative judgment, and willingness to engage iteratively with feedback, confirming recent concerns about overreliance on AI and shallow engagement (Chaudhry et al., 2023; Dergaa et al., 2023). More broadly, the study underscores the emerging need for structured AI literacy and prompt-design instruction within business curricula (Dwivedi et al., 2023; Pinto et al., 2023).

There is a history of research on academic writing that connects the complex ways that writers think through writing processes to forge academic identities (Brandt, 2001; Ivanic, 1997). These studies of academic writing closely inspect practices like brainstorming, reflecting, and revising writing through observations in classroom environments and interviews to deepen an understanding about what it means to be an academic writer. Traditionally, academic writing has been examined as a steady process that requires time in institutional contexts with students honing the craft of writing for academic purposes (Thomson, 2023; Thomson & Kamler, 2016). However, in the age of AI, this picture has changed dramatically, with Large Language Models (LLMs) expediting academic socialisation for emerging writers and researchers through generated text that is prompted and customised to a writer’s needs and discipline. LLMs give developing academic writers immediate, interpreted text without the emotional and linguistic labour it takes to bridge cultures, linguistic repertoires, and registers. There are therefore more possibilities and affordances than ever to speed up an academic writing voice. By simulating languages and brokering across linguistic divides, LLM software gives rapid-fire cross-cultural understandings without the time and experience previously needed to navigate new languages and unfamiliar cultures. The core argument of this paper is that ChatGPT and LLMs more generally are social contexts where international students improvise academic identities and forge conceptions of writing. Key questions asked in the paper are: How do PGR international students perform academic identities and experience academic socialisation in the age of AI? In what ways is a writerly voice forged across AI platforms? This does not mean that AI-led writing is an academic panacea or in some way bypasses socialisation, rather AI must be seen as a co-traveller with international students as they navigate developing academic identities.

Current educational research and policy increasingly emphasise the need to reimagine K-12 teaching and learning in the context of ongoing digital transformations [1]. While digital technologies, and artificial intelligence in particular, are often framed as solutions to educational challenges, their ethical, relational and political implications remain insufficiently examined in everyday pedagogical practices [2]. Therefore, the central research question guiding this paper is: How can sociotechnical imaginaries be critically reclaimed to support more democratic and caring educational futures? This question is explored through three subsidiary questions: What forms of absence, invisibility or silence characterise the use of digital tools and environments in everyday educational practices? How do teachers and learners negotiate, resist or reinterpret dominant narratives of AI-driven innovation? And what pedagogical conditions enable collective reflection on alternative sociotechnical futures?
This paper emerges from a research stay of a Portuguese junior researcher with an Irish leading author in sociotechnical imaginaries to foreground questions of attentiveness, responsibility, competence and responsiveness in education with digital technologies, while understanding ethical tensions around digital security, privacy, equity and social justice. These tensions are reshaping the humanistic foundations of educational research and practice and call for renewed inquiry into a Digital Ethics of Care (DEC) grounded in lived educational experiences [3]. Therefore, the paper adopts a narrative-based methodological orientation to explore how negotiation, engagement, trust and responsiveness emerge within digitally mediated learning environments oriented towards more just educational futures.
Our approach recognises sociocultural and ethical practices shaped by power relations, institutional expectations and everyday pedagogical decisions [4]. In doing so, it responds to growing concerns about the depoliticisation of educational practice and the marginalisation of professional judgement under data-driven and automated systems. The research stay pursues the strategic objective of mapping the present through a scoping review of recent literature to identify key ethical, pedagogical and relational challenges and opportunities associated with digital technologies and AI in compulsory education.
Conceptually, the paper is informed by futures studies in education and structured around two interconnected axes: critical pedagogy and ethics of care. From a critical pedagogy perspective, this axis draws attention to processes of depoliticisation within contemporary pedagogical discourse, particularly those that present technological change as inevitable or value-neutral [5]. It examines how power relations are embedded in data-driven technologies and AI systems, and how these technologies shape what counts as knowledge and participation in schools. The ethics of care axis foregrounds relationality, vulnerability and responsibility in educational practice. It explores how care is enacted, constrained and reconfigured in digitally mediated environments [3]. At the same time, the paper considers how care practices can resist or rework dominant sociotechnical imaginaries, creating space for more humane, inclusive and context-sensitive forms of education [6].