Supporting Student Success
Is Argument Visualization Effective? : Exploring the Use of Argument Visualization in Coaching Argumentation in Higher Education View Digital Media
Paper Presentation in a Themed Session Daniel Chang, Michael Lin
Argument mapping has been widely recognized as an effective tool for enhancing critical thinking and reasoning skills in higher education. This study compares student performance across two semesters—one in which students constructed argument maps using a traditional PowerPoint-based approach and another in which they employed DMap (Dialectical Map), an open source specialized digital tool designed to support structured argumentation. Preliminary findings indicate statistically detectable but modest differences in essay writing performance, with students using DMap demonstrating better quality of argument in final essays. Survey responses further reveal that, despite encountering some technical challenges, students generally perceive argument mapping as beneficial for visualizing complex reasoning structures and improving their analytical skills. These findings suggest that integrating digital tools like DMap into argument mapping pedagogy may provide a more effective method of fostering structured reasoning and critical engagement with complex controversial topics. Further research is needed to explore long-term impacts and instructional strategies to optimize the use of argument-mapping tools in higher education.
Artificial Intelligence and Pedagogical Innovations in Inclusive Education: A Systematic Review View Digital Media
Paper Presentation in a Themed Session José Manuel Ortiz-Marcos, Ana Isabel Invernon-Gomez, Lina Higueras-Rodríguez
The use of artificial intelligence (AI) in early education has opened up new possibilities for the inclusion of students with disabilities, facilitating their access to content and promoting adaptive teaching methodologies. This research presents a systematic review of the literature on AI-based pedagogical innovations for students with disabilities, with the aim of analysing trends, challenges and opportunities in this field. For this purpose, an exhaustive search was conducted in academic databases such as Scopus, Web of Science and ERIC, using Boolean operators (AND, OR, NOT) and keywords such as ‘artificial intelligence’, ‘inclusive education’, ‘adaptive learning’ and ‘assistive technologies’. Articles published in the last decade (2014-2024) were included, prioritising empirical studies and reviews that evaluated the impact of AI on the learning of students with sensory, cognitive and motor disabilities. The findings highlight the development of tools such as intelligent tutoring systems, virtual assistants with natural language processing, accessibility applications with voice and image recognition, and personalised learning environments. However, challenges such as the digital divide, lack of teacher training and ethical implications related to the use of sensitive data are also identified. This literature review contributes to the understanding of the role of AI in inclusive education, providing a theoretical and empirical basis for future research and pedagogical practices that seek to ensure more equitable and accessible education.
From Passive Consumption to Active Collaboration: Empowering Students through Human-AI Team Learning Models View Digital Media
Paper Presentation in a Themed Session Hang Yuan
Artificial intelligence (AI) is becoming a regular part of classrooms, offering new ways to enhance learning. Yet, many AI tools still focus on delivering content, which can limit student engagement. The Human-AI Team Learning (HATL) model encourages a different direction, where students interact with AI as a collaborative partner. This model helps future educators design learning experiences that promote curiosity, critical thinking, and creativity. The HATL model is built on five key principles that make human-AI teamwork meaningful. First, aligned learning goals ensure that AI supports what students want to achieve, keeping learning purposeful. Second, context-aware interaction means AI can respond to students based on where they are in their learning journey. Third, adaptability allows AI to adjust as students grow and their needs evolve. Fourth, reflection and guidance, led by educators, help students think critically about AI suggestions, encouraging ethical and thoughtful decision-making. Finally, support for student autonomy empowers learners to take charge, using AI to explore, solve problems, and make informed choices. This process transforms how students engage with technology. Learning becomes a collaborative process where students actively shape their experience, develop critical skills, and prepare for a world where working alongside AI is increasingly common.
