Conference Agenda

The rapid development of artificial intelligence (AI) presents both opportunities and challenges for its use within school settings. Central to this guidance is the principle that AI should support and enhance education rather than undermine professional judgement or replace the role of the teacher. The use of AI must be transparent, purposeful, and clearly justified where required.

Curriculum planning and lesson design are complex and time-consuming aspects of teaching, particularly within contexts that demand inclusive practice and responsiveness to diverse learner needs. Teachers are increasingly required to design curricula that are ambitious, coherent and adaptable, while also managing workload and wellbeing. Within this context, AI has the potential to function as a supportive tool that enhances efficiency and pedagogical decision-making. This article explores how AI can be used to aid curriculum planning and lesson design, with a particular focus on geography education.

AI can be utilised in three primary ways to support curriculum development. First, AI tools can assist in the creation and refinement of schemes of work by supporting curriculum sequencing, mapping learning objectives, and identifying opportunities for progression across key stages. For example, in Geography, this may include structuring thematic units, integrating substantive and disciplinary knowledge, and ensuring alignment with national curriculum requirements. When used critically, AI can help teachers devote greater attention to curriculum and subject-specific pedagogy.

Second, AI can support the creation of teaching and assessment materials, including lesson presentations, worksheets, formative and summative assessments, and mark schemes. This has the potential to reduce planning time while enabling greater differentiation and consistency across departments. In the Computer Science classroom, AI-generated resources may include virtual environment scenarios, data analysis tasks, or enquiry questions tailored to specific topics or ability ranges. However, such resources require careful evaluation to ensure accuracy, relevance, and pedagogical coherence, particularly given the dynamic and contested nature of computational knowledge.

Third, AI can augment instruction both within and beyond the classroom by contributing to adaptive learning environments. AI-driven platforms may provide personalised feedback, scaffold learning, and offer targeted practice opportunities, thereby supporting learners with varying levels of prior knowledge and confidence. Research suggests that, when used appropriately, such tools can promote more inclusive and learner-centred approaches, supporting engagement and addressing misconceptions (Baidoo-Anu and Ansah, 2023; Luckin et al., 2022; Perrotta and Selwyn, 2020).

Despite these potential benefits, the integration of AI into curriculum planning and lesson design presents several challenges. Overreliance on AI-generated materials risks diminishing teacher autonomy, professional creativity, and critical engagement with subject knowledge. There are also significant ethical considerations, including data privacy, algorithmic bias, and the responsible use of pupil information. Furthermore, while AI may reduce certain workload pressures, its implementation must be carefully managed to avoid increasing cognitive or administrative demands on teachers.

This article argues that AI should be positioned as an assistive technology that complements teacher expertise rather than replacing it. When embedded thoughtfully and ethically, AI can support more flexible, responsive and inclusive curriculum design. In the geography classroom, this approach has the potential to enhance both teaching practice and learner outcomes, while preserving the central role of the teacher as curriculum designer and professional decision-maker.

Graduate education aims to raise independent researchers. However, many graduate students, particularly in education-related programs across European higher education contexts, experience statistics anxiety and low self-efficacy in quantitative research methods and statistics (Faber & Drexler, 2019). Similarly, challenges in research methodology education, including passive teaching formats and limited active pedagogical practices in multiple European higher education systems (Kniffert et al., 2025) highlight the need for instructional design in quantitative methods in education-related graduate programs.

Although previous research examined the predictors of achievement in quantitative research methods and statistics, including statistics anxiety and self-efficacy (e.g., Hadfield, 2023; Onwuegbuzie & Wilson, 2003), findings remain inconsistent and rarely focus on graduate-level. Recent studies have examined technology-enhanced tools and platforms to support quantitative research methods and statistics learning. However, they focus on basic statistics, isolated tools instead of integrated learning environments, lack design guidelines combining theoretical frameworks and literature, and rarely incorporate students’ perspectives into the design (e.g., Ritzhaupt et al., 2020).

This necessitates a theory-driven online learning environment for graduate quantitative methods, particularly for European graduate programs with varying instructional traditions in the field of education. The study proposes an online learning environment design combining pedagogy, design principles, and graduate insights to support transferable design decisions across European higher education.

The pedagogical foundations are grounded in Sociocultural Theory and Situated Cognition, conceptualizing learning as a socially mediated process shaped through interaction, collaboration, and reflection (Nathan & Sawyer, 2014). Knowledge is co-constructed through peer interaction, and collaborative learning environments promote conceptual understanding with cognitive conflict and social negotiation (Savery & Duffy, 1995). This is particularly relevant to graduate education in Europe, where collaborative inquiry is central.

Building on these pedagogical foundations, the Community of Inquiry (CoI) framework (Garrison et al., 2000) has been adopted as a design framework in this study to structure the online learning environment. CoI emphasizes the interplay of cognitive (CP), social (SP), and teaching presences (TP) in supporting meaningful learning within a shared-goal community. SP fosters communication and group cohesion, CP reflects the inquiry process from problem identification to resolution, and TP focuses on the design and facilitation of meaningful learning.

To operationalize the CoI framework, Problem-Based Learning (PBL) is employed as the instructional design strategy. Established PBL models (e.g., Barrows, 1996; Hmelo-Silver, 2004), share characteristics including student-centered learning in small groups, instructor facilitation through questioning, and real-life-based and cross-disciplinary ill-structured problems. In PBL, students analyze a problem scenario, identify key facts together, form hypotheses about possible solutions, and realize their learning gaps, which underpin self-directed study. After independent research, students apply new knowledge to test and refine their hypotheses. Through this cycle, learners develop problem-solving skills, metacognitive awareness, and deeper conceptual understanding.

This study focuses on the learning environment design and explains how design decisions, informed by graduate students’ needs while learning quantitative research methods through interviews, were operationalized into problem-based narratives and tasks. These tasks were systematically aligned with specific CoI presences and grounded in the statistics education literature.

Research Question
What design principles emerge from the iterative development of a graduate-level online quantitative research methods course informed by PBL, the CoI framework, learner needs, and statistics education research?

Sub-Research Questions

1. How do learner needs and experiences inform the design decisions of the course?
2. How are principles of PBL operationalized and refined while designing narratives and tasks within the online quantitative research methods environment?
3. How does the CoI framework shape the design of cognitive, social, and teaching presences within the narratives, tasks, and environment features of the course?
4. How does the quantitative research method education research inform the narratives, tasks, and environment features of the course design?

The emergence of new technologies and innovations such as artificial intelligence (AI) and their introduction to education has brought new opportunities to explore in education and also to rethink and question our understanding of learning and education in general. For instance, the use of AI in education has been drastically transforming the landscape of education by changing the way learning is designed, delivered and evaluated. Especially, with the introduction of Large Language Models (LLMs) such as ChatGPT, we observe more individualized learning strategies emerging and it inevitably transforms learning experiences ( Verdú et al., 2017; Dever et al., 2020; Aristanto et. Al., 2023). For instance, now learning can be adapted to each student’ interest, learning style, pace and preferences. Besides making learning more personalized, AI also helps creating more inclusive learning experience for students with specific needs (Rakap, 2023). As a result, increasing number of educational researchers have been examining the use of AI in education. For instance, the recent study by Crompton and Burke (2023) showed that departments of education were the affiliations of the most authors investigating the use of AI in education (28%) followed by computer sciences (20%). While the interest in AI in education is following an upward trend, one of the main debate is on whether the use of AI improves learning experiences by supporting or hindering learners’ critical thinking (Ahmad et al., 2023; Chaparro-Banegas et al., 2024).

With all these discussions going on around the use of AI in education, we argue that first of all, we, as educational scholars, need to question and rethink how we understand education and learning and the very nature of them. At the end, our educational paradigms and our understanding of the aim of education will shape how we use AI in education. From a critical perspective, learning is “a process of challenging truth claims and arriving at a critical consciousness that these are not universal truths but claims that serve the interests of some at the expense of others (Kilgore, 2001, p.59). In other words, learning and education in general have to provide learners with an opportunity to question and be critical with what they learn. We argue that it is not the use of AI that hinders learners’ critical thinking, but our understanding of learning and education in general shaping our practice including our use of AI in education. Departing from this point, the paper examines the use of AI in education and how it hints the educational beliefs behind it. This papers also open a discussion on how the use of AI in education is reshaping and narrowing our understanding of learning and education with great focus on academic achievement as a sole aim of education.