https://www.um.edu.mt/library/oar/handle/123456789/18583 2026-04-22T14:46:28Z https://www.um.edu.mt/library/oar/handle/123456789/145502 Title: Futures of school knowledge and inclusive pedagogies Authors: Mizzi, Emanuel Abstract: In this presentation, the researcher draws on insights from the notion of the ‘Three Futures’ of school knowledge to inform teaching approaches that foster student engagement, voice, agency and learning (Young & Lambert, 2014; Young & Muller, 2010). Grounded in the distinction among Future 1, Future 2 and Future 3, the presentation investigates how different conceptions of school knowledge determine what counts as worthwhile learning. On this basis, inclusive pedagogies are understood as requiring not only the participation of diverse students but also access to powerful disciplinary knowledge and structured opportunities to question and re-contextualise it. The approach during the presentation combines theoretical analysis with examples from the author’s research in business education. First, the ‘Three futures’ heuristic is outlined, highlighting the limits of Future 1 (traditional, fixed curricula) and Future 2 (over contextualised, skills-driven learning). Future 3 is then presented as a way of placing powerful disciplinary knowledge at the core of the curriculum while opening it up for critique, contestation and application to students’ lived worlds. This conceptual framing is illustrated through inclusive pedagogical practices, including cooperative learning strategies that invite students to interrogate business concepts, engage in deep thinking, and articulate their perspectives. Key insights point to a Future 3 curriculum as particularly promising for inclusive pedagogy: it enables students to access and work with powerful disciplinary knowledge in ways that support engagement and encourage critical examination of taken-for-granted assumptions in business and economic life. The presentation concludes by discussing implications for policy, classroom practice and future research, including the need for curriculum frameworks that value powerful knowledge, teacher education that develops inclusive, critical disciplinary pedagogies, and further empirical studies on how such approaches can enhance deep learning and agency in diverse classrooms. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145444 Title: The role of generative AI in qualitative data analysis : opportunities and limitations in supporting dissertation supervision in academia Authors: Busuttil, Leonard; Camilleri, Rosienne Abstract: Generative AI is rapidly reshaping dissertation supervision, particularly in qualitative research where large language models (LLMs) are increasingly used for tasks such as coding, summarisation, and thematic exploration. This chapter examines how LLMs can support ―but not replace― the interpretive work that underpins rigorous qualitative inquiry. Drawing on Cultural-Historical Activity Theory (CHAT) and connectivism as theoretical framework, we show how technical parameters such as tokenisation, context windows, temperature, and platform guardrails function as methodological variables that directly influence analytical outcomes. We identify key supervisory concerns including students’ over-reliance on automated analysis, the erosion of interpretive competencies, documentation and transparency challenges, and inequities in access to advanced AI tools. In response, the chapter advances two guiding commitments: reflexive integration, which positions AI outputs as provisional and subject to triangulation, and digital stewardship, through which supervisors model ethical, transparent, and methodologically coherent AI use. To operationalise these commitments, we introduce the SUPERVISE framework, offering practical strategies for hybrid human–AI workflows, responsible documentation, and equitable supervisory practice. The chapter argues that supervision must shift from transmission to co-navigation, preserving interpretive ownership while leveraging AI’s analytical affordances. In doing so, it aligns responsible AI use with the epistemic values and pedagogical aims of qualitative research. 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145443 Title: Large language models for educational task authoring : a Bebras challenge case study Authors: Busuttil, Leonard Abstract: This study explores the application of large language models (LLMs) to create computational thinking tasks for the Bebras International Challenge through a single-case study approach. Using exemplar-based prompting with seven authentic Bebras tasks from the 2024 cycle as contextual input, a task was developed that was subsequently accepted for inclusion in the 2025 international Bebras challenge. Comparison with the exemplar tasks confirmed that the generated content drew from multiple sources rather than replicating any single task, combining grid-based constraint satisfaction, rule-based filtering, and logical deduction into a novel navigation puzzle with engaging narrative context. International expert reviewers evaluated the task using established Bebras quality criteria, confirming successful alignment with core pedagogical requirements including age-appropriateness, clarity, and cultural neutrality. However, two significant gaps emerged in the broader authoring workflow: accessibility compliance in the researcher-authored visual components and technical inaccuracies in the LLM-generated informatics framing. Following collaborative revision by international editors that addressed these concerns while preserving the LLM’s creative contributions, the task achieved acceptance for international use. The findings reveal a collaborative pipeline comprising contextual preparation, LLM-guided generation, human technical implementation, expert community review, and collaborative revision. Results from this case suggest that LLMs can efficiently generate educationally sound creative foundations while requiring integrated human expertise to meet specialised standards and ensure inclusive design, with the task’s acceptance providing encouraging evidence for the viability of this collaborative approach. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144776 Title: Teacher educators’ emotions toward generative AI : cross-national focus groups Authors: Juuti, Kalle; Turkkila, Miikka; Usenik, Janja; Vassallo, Diane; Stupurienė, Gabrielė Abstract: Research topic/aim: This paper describes the efforts of teachers’ academy project “EmpowerAid” which aims to foster creative, critical, and ethical uses of artificial intelligence in teacher education across Europe. As a starting point for the development, focus group interviews were organized to better understand teacher educators’ needs and concerns relating to GenAI tools. Based on the summaries of the interviews, we analysed the teacher educators’ emotion related to GenAI. 2026-03-01T00:00:00Z