Augmenting Understanding
Empowering Critical AI Users: Transformative Approaches to GenAI in University Writing and Ethics Courses View Digital Media
Paper Presentation in a Themed Session
Emily Dux Speltz
This research explores innovative pedagogical approaches for integrating artificial intelligence (AI) into higher education, focusing on experiential learning methods in AI-enhanced courses (“AI & Ethics” and “AI & Writing”) at two US universities. The study examines the effectiveness of these approaches in developing students’ AI literacy, critical thinking, and ethical reasoning skills across diverse disciplines and delivery modes (in-person and asynchronous online). By emphasizing hands-on engagement with generative AI tools and critical analysis of their implications, this work addresses the pressing need for educational strategies that prepare students for an AI-driven future while maintaining a human-centered, ethical perspective. The research employs a mixed-methods approach, combining quantitative analysis of students' self-efficacy measurements with qualitative assessment of their final projects and course interactions. The findings demonstrate how students effectively develop ethical awareness and practical skills across various domains through AI engagement. We present examples of successful teaching strategies that foster critical thinking about AI’s societal impact and ethical implications. This study contributes to the fields of digital pedagogy and AI ethics education, offering insights into effective AI integration in teaching, methods for fostering critical stances toward AI, and strategies for enhancing teacher training. The study also suggests that this experiential, ethics-focused approach can effectively prepare students for critical engagement with AI technologies, pointing toward transformative possibilities in curriculum development.
Comparing Linguistic Indicators of Classic Literature Adapted by Generative AI
Paper Presentation in a Themed Session
Tsai Yuan Huang,
Hui-Hsien Feng
With advancements in technology, generative AI (GenAI) has the potential to adapt text to different reading difficulty levels. However, the appropriateness of AI-generated output has not yet been thoroughly explored, it remains unclear whether GenAI tools align with the linguistic trend of predetermined difficulty level. To address this gap, this study explores the extent to which generative AI tools, i.e., ChatGPT and Brisk Teaching, align with expected linguistic trends when adapting English literature for different grade levels. The primary objective was to examine readability scores, vocabulary level, syntactic complexity, and lexical complexity in AI-generated texts to evaluate their suitability for educational purposes. The research calculated the readability score using the Flesch Reading Ease score, cross-referenced Oxford 3000 and 5000 to understand the distribution of the vocabulary level, and calculated syntactic and lexical complexity metrics using TAALES and TAALED tools. Texts were generated at three reading levels (Grades 6, 9, and 12) and analyzed through ANOVA to identify statistically significant patterns. Results indicate that while Brisk Teaching demonstrated clearer trends in vocabulary control, neither tool consistently aligned with expected patterns of increasing complexity or readability across grade levels. In addition, while variations in lexical difficulty and syntactic complexity are observed across different levels, these changes are not reflected in the readability levels. This suggests that relying solely on a single readability measure cannot comprehensively represent the reading difficulty of a text. Future research is recommended to incorporate multiple evaluation methodologies for a more comprehensive understanding of AI-generated text characteristics.
Finding a New Road to EFL Autonomy: The Role of AI in Language Learning in Taiwan
Paper Presentation in a Themed Session
Jin-Huei (Clarence) Ke
Integrating artificial intelligence (AI) into language learning has become increasingly prevalent in education. This study examines the processes and effects of AI-assisted learning on students’ autonomy and self-regulation. Specifically, it investigates the learning autonomy, motivation, and self-regulation of Taiwanese elementary school students learning English using the Adaptive Learning Website developed by Taiwan's Ministry of Education. The platform includes an embedded AI tutoring system, TALPer, designed to scaffold language learning for students from grades 1 to 12. Twenty fifth-grade students participated in an AI-assisted English learning class, which was integrated into their regular English curriculum and supplemented with portfolio-based activities using the Adaptive Learning Website. The websites' supportive learning framework and AI scaffolded learners during learning. Qualitative data were collected from students’ reflections, records of AI-student interactions, and interviews to analyze the impact of AI-assisted learning on students' autonomy and self-regulation. The findings showed that technology and self-regulated learning abilities supported learners' autonomy. In this Asian context, teachers' instructions on self-regulated learning will enhance students’ self-confidence and improve their academic achievement. This study highlights the potential of AI and multimedia tools to foster greater learner autonomy, which affects students’ engagement in self-directed learning. However, it raises concerns about students’ capacity to use technology effectively and internet addiction.
