AI Integration
Asynchronous Session
Integrating Artificial Intelligence-mediated Project-based Learning: Innovative Solutions to Improve Student Learning View Digital Media
Paper Presentation in a Themed Session Dr. Ahmed Hashash
Various constraints of the learning environment can cause a difficulty for students to learn in depth and understand its significance in real-world practice. Therefore, educational involvements, including artificial intelligence (AI)-enabled technology methods and project-based learning (PjBL) that foster students’ self-directed learning skills and engagement and encourage their flexible learning, are needed. The overall goal of our study/project is to promote student success by providing an opportunity to students to develop more self-directed, active, and deep learning skills using innovative integrated AI-mediated project-based learning. Our promising data and expectations indicate that this integration can improve student critical-thinking and engagement and arouse students’ self-directed, active, and deep learning skills. This study/project will help with facing various important education and learning challenges in small/large classes, and lead to enhance student success. It can have important implications for students, instructors, and curriculum designers in higher education, and help to face various challenges of science education and learning.
Harnessing AI for Inclusive Education: Strategies and Tools for Supporting University Students with Autism Spectrum Disorder (ASD) View Digital Media
Paper Presentation in a Themed Session Ioana Kocurova Giurgiu, Zuzana Havlisová, Miluše Löffelmannová
The integration of Artificial Intelligence (AI) into higher education presents transformative opportunities for enhancing learning experiences, particularly for neurodivergent students. This paper explores AI-driven strategies and tools designed to support university students with Autism Spectrum Disorder (ASD) by fostering structured learning, effective communication, and adaptive teaching approaches. Key strategies include maintaining predictable course structures, employing multimodal teaching methods, and offering individualized support to accommodate diverse learning needs. AI applications for lecture transcription or text-to-speech conversion, and adaptive learning platforms may provide personalized learning experiences. Additionally, assistive technologies are playing an increasingly vital role in inclusive higher education. Recent studies highlight how AI-driven assistive tools enhance accessibility and academic performance for students with learning difficulties (Yenduri et al., 2023). Furthermore, AI-enhanced assistive technologies are evolving to address the unique challenges of neurodivergent learners, offering solutions to improve focus, comprehension, and social communication in higher education settings (Adero and Skiles, 2023). Beyond academic support, AI-powered virtual assistants, chatbots, and social interaction tools help students navigate both academic and social challenges. Educators play a critical role in fostering a neurodivergent-friendly environment through peer mentoring, flexible assessment methods, and conflict resolution strategies. This paper discusses best practices, implementation challenges, and the evolving role of AI in neurodiverse education, ultimately highlighting how AI can bridge gaps in learning and social engagement for students with ASD in a business school in the Czech Republic.
Balancing the Potential Benefits of Technology and Artificial Intelligence with the Core Principles of Social Work Education and Practice View Digital Media
Paper Presentation in a Themed Session Mioara Diaconu, Domingo Carbonero Munoz, Laura Racovita
The future of higher education is increasingly intertwined with experiential learning, immersive working environments, and the exploration of citizenship and identity through the lenses of human rights and psychosocial and environmental justice. As higher education adapts to meet the needs of a rapidly changing world, technology, and artificial intelligence (AI) have emerged as pivotal tools in creating interdisciplinary connections between social work education and other academic disciplines and professional settings. Social work education stands to benefit from the integration of AI and technology, which can enhance students' understanding of diverse communities, analyze big data for improved decision-making, and facilitate virtual learning environments that transcend geographical barriers. However, concerns about the ethical implications of AI in social work, particularly in terms of maintaining human-centered practices, protecting client confidentiality, and addressing biases in algorithmic decision-making could emerge. In this context, human learning and machine learning must be seen as complementary, with AI serving as a tool to support, rather than replace, the relational, human aspects of social work practice. Thus, educators must navigate the ethical and practical challenges of incorporating AI into curricula while ensuring that students maintain a strong commitment to the values of social justice and human dignity. By balancing the potential of technology with the core principles of social work, we can better prepare students to address the multifaceted challenges of the future. Recommendations related to the incorporation of technology, and AI social work curricula and practice are considered. Barriers to implementation are also explored.
Imaginatively Interpreting Literature with Generative AI: A Process-Oriented Pedagogy View Digital Media
Paper Presentation in a Themed Session Kieran O'halloran
Creative thinking and adaptability are key workplace assets, with employers seeking graduates able to exploit Generative AI’s (Gen AI) potential. University educators need to establish opportunities for students to explore GenAI whilst creatively adapting outside their disciplines. Yet, GenAI’s fast and fluent responses can undermine students’ thinking skills development, potentially subverting module assignment integrity also. I present a process-oriented pedagogy for interpreting short stories with GenAI, which is accessible to undergraduates university-wide. It is designed to cultivate creative thinking through reflective engagement, whilst protecting academic integrity. Crucially, the approach facilitates generation of bottom-up problems: interpretive challenges in reading short stories that emerge unexpectedly from the zig-zagged interplay between human readers, qualitative GenAI output, quantitative text analysis software output, and psychological literature illuminating characters’ motivations. This iterative problem-addressing process fosters a “slow creativity” that dwells in the struggle to form coherent interpretation from multiple interpretive tangents and initial uncertainty—thereby evolving a non-predestined imaginative engagement that leads to satisfying solution. The elliptical nature of classic short stories renders them particularly amenable to this pedagogy, inviting a multiplicity of interpretive pathways. A case study using Edgar Allan Poe’s short story The Black Cat demonstrates how the approach yields inventive reading and also ensures academic integrity in a process-oriented assessment. More broadly, I highlight that while GenAI offers attractive efficiencies—such as rapidly brainstorming psychological literature in this pedagogy—it can be harnessed to substantially enhance the slow and gradually deepening thinking that is necessary for fulfilling human creativity.
