Conference Agenda

In this presentation, we propose a new approach to social care education that can help prepare practitioners for a post-AI and post-social robotics era. By moving beyond ‘the algorithm’, virtual reality [VR] caregivers may harness immersive interactions that circumvent many limitations of physical social robots - cost, logistics, acceptance - and can be easily updated. We highlight a four-session curriculum to show how future social workers can imagine, debate and design VR-based solutions that centre on empathy, user-friendliness and cultural sensitivity. This approach encourages social care students to develop deeper insights into the lived experiences of care recipients, such as older adults or individuals with dementia, and into the concept of ‘care’ itself. We also examine vital ethical, privacy and accessibility concerns, ensuring that VR-driven solutions remain person-centred and equitable. Ultimately, with our students, we wish to explore a potential blended future where AI complements, rather than supplants, human care. In doing so, we aim to open up a conversation on how can - or even should - higher education meaningfully integrate immersive technologies for next-generation care. We welcome further discussion and collaboration.

The integration of Artificial Intelligence (AI) into education presents both significant opportunities and challenges, particularly for Initial Teacher Education (ITE) programmes tasked with preparing future teachers for its effective and ethical use. However, varying levels of AI readiness among student teachers—encompassing their knowledge, skills, and attitudes toward AI—complicate this process. Drawing on Schepman and Rodway’s (2020) work on AI readiness, this conceptual paper introduces the ‘kaleidoscope of AI perspectives,’ a reflective framework designed to deepen awareness of the varied dispositions that influence AI adoption and use in educational contexts.

The paper explores the intersection of AI readiness with the UNESCO AI Competency Framework, offering a structured, dynamic approach to developing AI literacy within ITE. Central to this discussion is the debate over whether AI literacy should be treated as a distinct area or integrated into broader digital literacy frameworks (Holmes, 2022). Additionally, the paper examines where and how AI literacy could be incorporated into ITE programmes, providing actionable recommendations for its inclusion.

The authors argue that embedding AI literacy into ITE is critical for equipping future teachers to navigate and employ AI responsibly, ethically, and effectively in educational contexts. This proactive measure is positioned as essential, given AI’s growing influence in education (EC, 2022). By fostering an informed and critical mindset, the proposed framework aims to prepare teachers not only to use AI technologies but also to understand and question their implications for teaching, learning, and equity.

European Commission. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/153756

Holmes, W., Persson, J., Chounta, I.-A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy, and the rule of law. The Council of Europe. ES428045_PREMS 092922 GBR 2517 AI and Education TXT 16x24.pdf

Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014

UNESCO. (2024). AI competency framework for students. Paris: United Nations Educational, Scientific and Cultural Organisation. https://doi.org/10.54675/ZJTE2084

Generative Artificial Intelligence (GenAI) is reshaping education, creating both opportunities and challenges for teacher education programs (Mishra et al., 2024). As pre-service teachers increasingly engage with GenAI tools like ChatGPT, understanding how institutional and regional contexts shape their experiences and perceptions of GenAI is critical (Celik et al., 2022; Moorhouse & Kohnke, 2024). As part of an international collaborative design-based research project (Hsu et al., 2024), this study compares the experiences and perceptions of pre-service teachers at Dublin City University (DCU) with those of students majoring in education or enrolled in postgraduate education programs at Arizona State University (ASU) regarding the use of GenAI for personal, academic, and professional purposes. This research aims to inform the development of targeted training programs tailored to the specific needs of each institution.

Data were collected from 204 DCU participants and 127 ASU participants using a questionnaire with items on a 5-point Likert scale. The survey examined the application of GenAI for personal, academic, and professional purposes, as well as participants’ perceptions of its opportunities, challenges, ethical concerns, and professional development needs.

DCU participants reported slightly higher experience levels with GenAI (M = 2.94, SD = 1.46) than ASU participants (M = 2.80, SD = 1.32), though the difference was not statistically significant. Moreover, DCU participants reported significantly more frequent use of GenAI tools (M = 2.43, SD = 1.38) than ASU participants (M = 2.17, SD = 1.14; p < .05, Cohen’s d = 0.21), reflecting a small-to-medium effect size. Both groups recognised GenAI’s opportunities for enhancing teaching and learning, with DCU participants scoring slightly higher (M = 3.47, SD = 0.79) than ASU participants (M = 3.41, SD = 0.90), although a non-significant result. Furthermore, ASU participants perceived slightly more challenges (M = 3.31, SD = 0.85) than DCU participants (M = 3.18, SD = 0.95), but this difference was also insignificant. Significant differences were observed in ethical considerations, with ASU participants expressing more significant concerns (M = 3.38, SD = 0.69) compared to DCU participants (M = 3.07, SD = 0.91; p < .001, Cohen’s d = 0.36), suggesting a medium effect size. Regarding professional development, DCU participants reported a significantly greater need for training on effective use (M = 3.60, SD = 1.10) than ASU participants (M = 3.24, SD = 1.02; p < .005, Cohen’s d = 0.34), also indicating a medium effect size. They further expressed a significantly higher need for training on ethical use (M = 4.24, SD = 0.97) compared to ASU participants (M = 4.00, SD = 0.87; p < .05, Cohen’s d = 0.25), reflecting a small-to-medium effect size.

This study underscores the importance of tailoring GenAI-focused professional development to specific institutional contexts, addressing distinct strengths and challenges. The findings highlight the practical significance of these differences on equipping future educators with the skills to leverage AI responsibly in diverse educational institutions.

References

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends, 66(4), 616-630. https://doi.org/10.1007/s11528-022-00715-y

Hsu, H.-P., Mak, J., Werner, J., White-Taylor, J., Geiselhofer, M., Gorman, A., & Torrejon Capurro, C. (2024). Preliminary Study on Pre-Service Teachers’ Applications and Perceptions of Generative Artificial Intelligence for Lesson Planning. Journal of Technology and Teacher Education, 32(3), 409-437.

Mishra, P., Oster, N., & Henriksen, D. (2024). Generative AI, Teacher Knowledge and Educational Research: Bridging Short- and Long-Term Perspectives. TechTrends, 68(2), 205-210. https://doi.org/10.1007/s11528-024-00938-1

Moorhouse, B. L., & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 103290. https://doi.org/https://doi.org/10.1016/j.system.2024.103290

As artificial intelligence (AI) technologies, particularly generative AI (GenAI), become increasingly prevalent, their implications for education grow more profound. Tools such as ChatGPT offer immediate access to an array of synthesised information, potentially reshaping how students interact with knowledge. However, this accessibility also presents challenges for educators, especially concerning the authenticity, reliability, and educational value of AI-generated content. This paper explores a novel approach to developing digital and information literacy skills in pre-service post-primary teachers through a WebQuest methodology enhanced with GenAI tools. Originally designed to help students engage critically with web-based resources through structured, inquiry-based learning (Dodge, 1997), the WebQuest methodology provides a scaffolded framework that can be adapted to include GenAI, enabling students to build skills in both traditional and AI-mediated research.

In this study, we introduce a modified WebQuest designed specifically to engage pre-service secondary teachers with digital literacy in the age of AI. This offers a critical opportunity for students to analyse, question, and contrast information from multiple sources. The modified WebQuest structure begins with an introduction to the topic. Through a selection of curated, reliable resources, including journal articles, vetted websites, and other digital resources, students initially conduct traditional research on the topic. Following this, they engage with GenAI tools by posing questions to explore AI’s capacity to generate information, summarise topics, and provide answers. By comparing AI-generated responses with traditional resources, students gain a deeper understanding of the accuracy, reliability, and potential biases inherent in AI systems.

To support this comparative approach, we developed two evaluation rubrics to encourage both self-reflection and structured assessment. The student self-evaluation rubric emphasises self-awareness in evaluating one’s accuracy in understanding content, depth of analysis, and ability to critically reflect on GenAI-generated responses versus traditional sources. For instance, students assess how AI responses align or diverge from journal articles and other verified sources, examining discrepancies or biases they uncover. This process of reflection helps students understand the affordances and limitations of using AI in an educational context, fostering reflection on their digital and information literacy skills.

The instructor evaluation rubric complements the student-focused assessment by emphasising pedagogical and analytical competencies. This rubric evaluates students on their understanding of WebQuest topic, effectiveness in comparing sources, and depth of insight in their final analyses. Additionally, it assesses how well students articulate their reflections on the role of GenAI, as well as the clarity and coherence of their final presentation or report. By incorporating both self-assessment and instructor-led assessment, this approach fosters a holistic development of digital and information literacy skills, equipping future educators with a critical toolkit for navigating AI in the classroom (Holmes, Bialik, & Fadel, 2019; Webber, 2018).

This integration of GenAI into WebQuest methodology represents a significant pedagogical development, as it enables pre-service teachers to engage with AI while honing essential skills in evaluating information. Given the rapid pace at which AI is reshaping information access, understanding the affordances and limitations of AI becomes essential. Through the proposed methodology, pre-service teachers are guided in developing a critical approach to AI-mediated information. This paper contributes to the conversation on AI and education by offering a framework for using AI tools within an established educational methodology, fostering a digitally literate and discerning generation capable of navigating an AI-driven world.

References

Dodge, B. (1997). Some thoughts about WebQuests. San Diego State University. Retrieved from http://webquest.sdsu.edu/about_webquests.html

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.

Webber, S. (2018). The impact of artificial intelligence on information literacy. Journal of Information Literacy, 12(2), 1-15.

Introduction

As AI technologies continue to advance, they open up new avenues for educators — from content creation and automation of administrative tasks to data-driven insights. This raises questions regarding the role of AI in education, and the ethical implications of its integration. This report, part of the PAIDEIA project funded by the Erasmus Plus Programme, delves into the perspectives of educators on the impact of AI in education, now and in the future. It provides an analysis of both the opportunities and challenges presented by AI, offering a range of perspectives from teachers across seven European countries.

Methodology

The research employs a mixed-methods approach, encompassing surveys and focus groups to gather comprehensive insights. Over 700 teachers from Belgium, Bulgaria, Ireland, Italy, Malta, Spain, and Türkiye participated, providing a diverse view of their current use of AI and their perceptions of AI in educational settings. Surveys were conducted first to establish baseline data, followed by focus groups that allowed for deeper exploration of themes.

Findings

The findings indicate that AI usage in education among PAIDEIA partner countries is generally low to moderate, with significant variation in how and where AI is applied. AI is sporadically used for tasks like lesson planning, personalising learning, and content creation, while areas such as assessment, feedback, and administrative tasks see even less support through AI tools. Countries like Bulgaria and Ireland show higher adoption of AI to enhance learning experiences, whereas usage in Belgium, Spain, and Türkiye remains minimal. Understanding of AI among teachers also varies widely; while most teachers grasp basic AI principles and ethical considerations, many lack confidence in explaining AI processes, staying current with advancements, and applying AI effectively in educational settings. Teachers across PAIDEIA countries identify challenges such as the reliability of AI-generated information and data privacy issues, with mixed views on whether AI might undermine educational equity, diminish the teacher’s role, or impact teacher-student dynamics. Despite these concerns, educators are generally optimistic about AI’s potential to personalise learning, innovate teaching methods, and engage students, though Italian teachers expressed some hesitancy around these benefits. Teachers’ perceptions of students’ views on AI reveal mixed enthusiasm and awareness, with students generally seen as curious but unclear about AI’s benefits and potential ethical issues. There is broad agreement on the need for mandatory AI training for teachers, with insufficient training provisions noted across countries. Opinions are mixed regarding the adequacy of CPD opportunities, confidence in pursuing further training, and access to online AI resources. Overall, the findings highlight a need for structured, accessible training on AI in education, with a strong emphasis on practical applications, ethical considerations, and tailored CPD resources to build teacher confidence and capacity for AI integration.

Conclusion

This research provides important insights into teachers’ perceptions of AI in education, revealing that while usage is quite low, teachers recognise the opportunities AI may bring in the future. However, it also highlights the need to address ethical concerns associated with AI, alongside the potential negative effect it may have on student creativity and critical thinking. The study strongly emphasises the need for comprehensive training programs for educators and clear guidelines on the use of AI in educational settings.

References

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence In Education: Promises and Implications for Teaching and Learning. Boston, MA: Center for Curriculum Redesign.

Abimbola, C., Eden, C. A., Chisom, O. N., & Adeniyi, I. S. (2024). Integrating AI in education: Opportunities, challenges, and ethical considerations. Magna Scientia Advanced Research and Reviews.

Harry, A. (2023). Role of AI in Education. Interdisciplinary Journal and Humanity (INJURITY).

Porayska-Pomsta, K., Holmes, W., & Nemorin, S. (2022). The Ethics of AI in Education.