Conference Agenda

The rapid adoption of generative artificial intelligence (GenAI) in higher education offers significant opportunities, but also poses profound pedagogical, ethical and professional challenges. Although GenAI technologies offer scalability and efficiency, as well as new forms of learning support, uncritically adopting them could undermine human agency, professional judgement and meaningful learning processes. This symposium presents four design- and research-driven studies examining how GenAI can be purposefully designed, governed and embedded in higher education to strengthen, rather than replace, human learning, reflection and responsibility.

In a variety of settings, including universities of applied sciences and teacher education institutions, the contributions address a common issue: how can GenAI support learners and educators in becoming active participants in their own learning and professional development? Together, the papers move beyond the instrumental or productivity-focused use of AI, instead conceptualising GenAI as an educational design challenge, where pedagogical grounding, ethical governance, and intentional scaffolding are key.

The first paper introduces EDU-AID, an Erasmus+ design-based research project which explores how AI-supported feedback could improve the continuous professional development of higher education teachers. Addressing the well-documented limitations of existing CPD initiatives, which are often perceived as generic and poorly aligned with immediate teaching needs, the study demonstrates how combining generative AI with a curated, evidence-based research repository can provide more context-sensitive, pedagogically sound feedback. Rather than presenting AI as an autonomous expert, EDU-AID emphasises responsible design choices that incorporate validated educational research and respect teachers' professional autonomy.

The second paper shifts the focus to teacher education, examining how conversational AI can be designed to act as a reflective learning partner. Through the development of Holli, an AI persona that engages students in structured reflective dialogue, the study explores how generative AI (GenAI) can facilitate deeper levels of reflection without taking over cognitive or professional tasks. The design is grounded in theories of reflective practice and explicitly incorporates fading mechanisms to prevent dependency and support the gradual development of independent reflective competence. This study emphasises the importance of positioning AI as a temporary aid to learning rather than a substitute.

The third paper broadens the perspective by analysing how the governance of GenAI is articulated ethically at an institutional level. Through a comparative analysis of documents from three European universities of applied sciences, the study reveals distinct governance models that distribute responsibility for the ethical use of AI differently among students, educators, and institutions. Rather than viewing ethics as a purely regulatory matter, the analysis conceptualises ethical GenAI governance as an educational design issue that influences human agency, professional responsibility, and pedagogical expectations.

The fourth paper addresses student learning directly, developing and evaluating educational scenarios for GenAI use that empower students to take control of their own learning process. Focusing on programmes that are significantly impacted by GenAI-driven changes in professional practice, the study introduces the concept of a 'desired level of independence' to inform curriculum design. By aligning educational scenarios with evolving professional competencies, the paper offers practical strategies for leveraging the potential of AI to support learning and innovation, while preventing over-reliance on AI.

Together, the four papers offer a multi-level perspective on GenAI in higher education, considering instructional design, reflective learning, institutional governance and curriculum innovation. The symposium contributes to ongoing discussions by demonstrating that the responsible use of GenAI is primarily a pedagogical and ethical issue rather than a technical one. The symposium argues for design approaches that deliberately prioritise evidence-based practice, human agency and professional judgement, as well as governance models that support educators and students in navigating GenAI as an integral, yet critically mediated, part of contemporary higher education.

Higher education institutions face persistent challenges in providing sustainable, context-sensitive professional development for teachers. Existing continuous professional development (CPD) initiatives are often perceived as generic, time-intensive, and poorly aligned with teachers’ immediate teaching needs, particularly for early-career staff (Gaebal et al., 2018; Bloch et al., 2020; Sanchez-Tarazaga et al., 2022). At the same time, generative AI technologies are increasingly promoted as scalable solutions for teacher support, raising critical questions about how AI-driven tools can remain pedagogically grounded, evidence-informed, and human-centred. This paper presents the design rationale and early outcomes of EDU-AID (Educator Development Using AI for Instructional Design), a three-year Erasmus+ project aimed at developing an open-access AI-supported feedback tool to assist higher education teachers in (re)designing their teaching. The central research question guiding the project is: What are the core design features of an AI-supported feedback tool that enables authentic, just-in-time, and context-sensitive teacher support? The project follows a design-based research (DBR) approach comprising four iterative phases. First, a needs analysis was conducted through qualitative data from teachers and CPD providers across our three European partner institutions. This resulted in the development of six empirically grounded personas representing diverse teaching roles, disciplines, and levels of pedagogical expertise. Second, a two-phase literature review was carried out, culminating in a structured repository of over 2,500 peer-reviewed sources on evidence-based design principles for effective teaching in higher education. Third, these insights informed the development of a proof-of-concept AI feedback tool that combines generative AI with a curated, expert-validated research repository. The tool is designed to provide tailored feedback aligned with different teacher profiles and instructional goals. Finally, the project includes iterative user testing to examine perceived relevance, usability, and authenticity. Although empirical evaluation with end users is still ongoing, early analytical comparisons conducted within the project indicate that the inclusion of a structured, research-informed repository substantially enhances the quality, specificity, and pedagogical grounding of AI-generated feedback. Compared to responses generated by a generic large language model without access to curated scientific literature, the EDU-AID prototype produces feedback that is more explicit in its use of evidence-based design principles and less prone to generic or decontextualised advice. These findings highlight the importance of combining generative AI with validated knowledge infrastructures when designing responsible AI-supported professional development in higher education.

Bloch, C., Degn, L., Nygaard, S., & Haase, S. (2021). Does quality work work? A systematic review of academic literature on quality initiatives in higher education. Assessment & Evaluation in Higher Education, 46(5), 701-718. https://doi.org/10.1080/02602938.2020.1813250 Gaebel, M., Zhang, T., Bunescu, L., & Stoeber, H. (2018). Learning and teaching in the European higher education area. Brussels, Belgium: European University Association asbl. Sánchez-Tarazaga, L., Ruiz-Bernardo, P., Viñoles Cosentino, V., Esteve-Mon, F.M. (2024). University Teaching Induction Programmes. A Systematic Literature Review. Professional Development in Education, 50(2), 279-295.

Reflection is regarded as a core competence in teacher education, supporting professional judgment and theory-practice integration. Despite its importance, reflective assignments often remain descriptive, while guidance of reflective learning is experienced by teacher educators as time-intensive. This paper explores how conversational AI can be designed to support reflective learning in pedagogically grounded ways. Recent work on generative AI highlights a shift from AI as an information tool toward AI as a learning partner enabling experiential learning (Hardman, 2024). In particular, AI personas, conversational agents with a pedagogical role, have been shown to support learning by prompting reasoning and scaffolding reflection (Drobnjak & Boticki, 2023; Pranesh, 2024). Such agents facilitate learning through dialogue and structured questioning. This paper presents the design and research framework of Holli, a conversational AI tool developed within a teacher education programme in the Netherlands. Holli functions as an AI reflection partner that engages students in structured reflective dialogue following authentic learning experiences. Drawing on theories of reflective practice (e.g. Schön, 1983; Korthagen, 2001), it uses open-ended prompts and metacognitive cues to help students articulate experiences, examine assumptions, and connect practice to pedagogical concepts. Holli does not generate reflections for students but scaffolds reflective thinking. The project follows a design-based research approach with three iterative phases. The first phase focuses on co-design and prototyping with students and teacher educators, examining how persona design, conversational structure, and prompting strategies influence reflective depth. The second phase involves a pilot study comparing AI-supported reflection with traditional written reflection assignments, combining qualitative analyses and quantitative measures of reflection quality to examine whether students demonstrate more advanced levels of reflective thinking over time. The third phase addresses conditions for sustainable and responsible implementation, including the evolving role of teacher educators and risks of over-reliance on AI-supported dialogue. Early design analyses suggest that positioning conversational AI as a reflective dialogue partner supports deeper forms of reflection.These effects appear to be time-bound and contingent on the scaffolding provided. Holli is therefore explicitly designed with fading mechanisms, enabling the gradual withdrawal of support once reflective competence is sufficiently developed. By articulating design principles for AI-supported reflective learning, including the deliberate use and gradual fading of scaffolding, this study contributes to the growing international literature on responsible and pedagogically grounded uses of conversational AI in education.

Drobnjak, A., & Boticki, I. (2023). Learning with conversational AI and personas: A systematic literature review. In J.-L. Shih et al. (Eds.), Proceedings of the 31st International Conference on Computers in Education (ICCE 2023) (pp. 1003–1008). Asia-Pacific Society for Computers in Education. Hardman, P. (2024). From guesswork to simulation: How generative AI changes the way we design learning. https://drphilippahardman.substack.com/p/from-guesswork-to-simulation-how Pranesh, G. (2024). How AI personas are revolutionizing education: A deep dive into the persona pattern. https://medium.com/@praneshg24/how-ai-personas-are-revolutionizing-education-a-deep-dive-into-the-persona-pattern-edf164184643 Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books. Korthagen, F. A. J. (2001). Linking practice and theory: The pedagogy of realistic teacher education. Lawrence Erlbaum Associates.

The rapid adoption of generative artificial intelligence (GenAI) in higher education raises fundamental questions about human agency, professional responsibility and educational governance. Recent systematic and meta-review research highlights that the ethical adoption of GenAI in education hinges on transparency, accountability, data protection and human-centred governance. A key challenge across studies is translating high-level ethical principles into institutional practices that preserve human agency, professional judgement and responsibility. Educational institutions and universities of applied sciences (UAS), among others, play a critical role in shaping the ethical framework and governance of GenAI to support student autonomy and teacher competence. This exploratory study examines how the ethical use of GenAI is presented on the websites of four European UASes: Zuyd (the Netherlands), Inholland (the Netherlands), UCLL (Belgium), and JAMK (Finland). The empirical material consists of various web-based texts, policy papers, ethical guidelines and institutional statements that address the use of GenAI in education. Through qualitative document analysis, the study explores how responsibility for the ethical use of GenAI is distributed among students, teachers, and institutions, and how these norms are translated into pedagogical expectations. Jamk UAS emphasises transparency, accountability and human responsibility in the use of GenAI, as set out in its institutional guidance on the ethical use of AI. This guidance prioritises disclosure and compliance with legislation, as well as human responsibility for AI-supported work. UCLL UAS integrates ethical GenAI use into organisational learning and develops governance practices rather than fixed rules. This approach is reflected in initiatives such as the GPT Academy and discussions on AI governance, which emphasise contextual guidelines, professional judgement and collective responsibility. In contrast, Inholland UAS addresses students, staff, and the organisation as a whole separately. Rather than presenting GenAI as primarily a compliance issue governed by detailed formal regulation, Inholland positions it as an emerging educational and professional practice requiring ongoing institutional reflection and pedagogical experimentation. In contrast, ethical considerations related to GenAI are not explicitly articulated on the Zuyd UAS website but are embedded within the broader framework of research integrity and professional responsibility, which is presented and linked on the website. Across the four cases, ethical guidance on GenAI varies in form and explicitness. Taken together, it functions as a formal or informal code of conduct, from explicit governance instruments to embedded norms shaping professional judgement. Rather than a single model, institutions articulate responsibility, acceptable use and pedagogy through policy, dialogue and professional discretion.

Alfiras, M. I. I., Emran, A. Q., & Mohamed, A. M. (2026). Ethics and governance of generative AI in education: A systematic review on responsible adoption. Discover Education, 5, Article 37. https://doi.org/10.1007/s44217-025-01051-y García‑López, I. M., & Trujillo‑Liñán, L. (2025). Ethical and regulatory challenges of generative AI in education: A systematic review. Frontiers in Education, 10, 1565938. https://doi.org/10.3389/feduc.2025.1565938 Wickramasinghe, M., Gunawardena, L., & Padukkage, A. (2026). Ethical principles for artificial intelligence in education: A meta‑review approach. AI and Ethics, 6, Article 63. https://doi.org/10.1007/s43681-025-00878-3

The recent boom in generative AI (GenAI) is leading to major challenges in higher education. On the one hand, GenAI is changing the job content of many professions. For example, GenAI can support or even take over tasks and provide inspiration that professionals can then build on or use to propose solution strategies. These changes in job roles also have an impact on the objectives of higher education, which is to prepare students for their future careers. On the other hand, GenAI also has an impact on the teaching and learning process. GenAI can suggest answers to complex questions in a flash, often high-quality ones. For students who are beginners in the field, it takes a time-consuming and labour-intensive learning process to arrive at such answers themselves. It is therefore not surprising that students use GenAI. This use can both benefit and undermine learning. It is a challenge for (higher) education to use GenAI in a thoughtful way so that students effectively learn what we want them to learn and that they remain in control of their own learning process and do not become dependent on GenAI. This project, in collaboration with the professional field and lecturers, seeks answers to these challenges by 1. Developing a methodology to gain insight into possible future developments in the expected competencies of professionals. We are thinking of questions such as “What is a professional expected to be able to do independently, without GenAI? How is the professional expected to be able to use GenAI?”. We refer to this as “the desired level of independence”. 2. Developing, implementing and evaluating educational scenarios that support the development of competencies at the desired level of independence. We are doing this in two rounds, spread over two academic years, with one round in each academic year to further optimise the design. We are going through this process for the two course units from the Marketing and Applied Computer Science programmes. We chose these programmes because professional practice in both domains is strongly influenced by GenAI and therefore also poses challenges for higher education. We are using our experiences in these two cases to formulate more general suggestions.

Demirci, O., Hannane, J., & Zhu, X. (2025). Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms. Management Science, 71(10), 8097–8108. https://doi.org/10.1287/MNSC.2024.05420 Elen, J., & Verburgh, A. (2023). Fostering critical thinking: Features of powerful learning environments. European Journal of Education, 58(3), 434-446. https://doi.org/10.1111/ejed.12568 Felten, Edward W. and Raj, Manav and Seamans, Robert, Occupational Heterogeneity in Exposure to Generative AI (April 10, 2023). Available at SSRN: https://ssrn.com/abstract=4414065 or http://dx.doi.org/10.2139/ssrn.4414065 Hutson, J. (2025). Designing for Complementarity: Skills, Work, and Education in the Age of AI — A Review. Journal of Business and Social Sciences, 2025. https://doi.org/10.61453/jobss.v2025no07