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AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in specific scenarios, as well as the concerns related to the…
Students at all levels of education are increasingly relying on generative artificial intelligence (AI) tools to complete assignments and achieve higher exam scores. However, it remains unclear how this reliance affects their motivation,…
The rapid integration of AI into education has prioritized capability over trustworthiness, creating significant risks. Real-world deployments reveal that even advanced models are insufficient without extensive architectural scaffolding to…
The interdisciplinary research domain of Artificial Intelligence in Education (AIED) has a long history of developing Intelligent Tutoring Systems (ITSs) by integrating insights from technological advancements, educational theories, and…
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning…
Large language models (LLMs) are rapidly transforming knowledge work by improving the quality and efficiency of tasks such as writing, coding, and data analysis. However, their growing use in education has exposed a learning-performance…
In collaborative settings, sustaining momentum and engagement between checkpoints (e.g., meetings) can be challenging, often leading to task drift and reduced preparedness. To address this gap, we developed ReflectEd, an AI-assisted system…
AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking…
Generative AI and agentic tools are reshaping agile software development, yet many engineering curricula still teach agile methods and AI competencies separately and largely lecture-based. This paper presents a project-based AI Engineering…
Like many other domains in Artificial Intelligence (AI), there are specific tasks in the field of AI in Education (AIEd) for which labels are scarce and expensive, such as predicting exam score or review correctness. A common way of…
Introduction: Requirements engineering faces challenges due to the handling of increasingly complex software systems. These challenges can be addressed using generative AI. Given that GenAI based RE has not been systematically analyzed in…
Current Artificial Intelligence (AI)-based tutoring systems (AI tutors) are primarily evaluated based on the pedagogical quality of their feedback messages. While important, pedagogy alone is insufficient because it ignores a critical…
As design thinking education grows in secondary and tertiary contexts, educators face the challenge of evaluating creative artefacts that combine visual and textual elements. Traditional rubric-based assessment is laborious, time-consuming,…
In this study, I explored the impact of Generative AI on learning efficacy in academic reading materials using experimental methods. College-educated participants engaged in three cycles of reading and writing tasks. After each cycle, they…
While predictive models are increasingly common in higher education, causal evidence regarding the interventions they trigger remains rare. This study evaluates an AI-guided student support system at a large university using doubly robust…
Generative AI is rapidly reshaping STEM higher education. Not only are our educational practices changing, but how we think about educational transformation must adapt. Existing models of institutional change in STEM, aimed at interactive…
The growing integration of AI tools in student design projects presents an unresolved challenge in HCI education: how should AI-generated content be cited and documented? Traditional citation frameworks -- grounded in credibility,…
Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted GenAI access enables task outsourcing that undermines the validity of traditional assessments; blanket prohibitions are difficult…
Practical lab education in computer science often faces challenges such as plagiarism, lack of proper lab records, unstructured lab conduction, inadequate execution and assessment, limited practical learning, low student engagement, and…
Generative AI is reshaping education, yet most university AI policies are written without students and focus on penalizing misuse. This top-down approach sidelines those most affected from decisions that shape their everyday learning,…