We present an empirical study examining how experienced tutors (experts) and non-tutors (novices) evaluate the correctness of tutor praise responses under different AI-assisted decision-support interfaces and explanation styles. We examine human-AI reliance patterns by decomposing interaction errors into over-reliance (accepting incorrect AI suggestions) and under-reliance (rejecting correct AI suggestions), together with time cost as a process-level indicator. Across conditions, human-AI collaboration improved accuracy compared to humans working alone, but consistently underperformed an AI-only baseline, indicating that human judgment introduced additional errors even when assisted by a highly accurate model. Novices benefited more from AI support since they tend to follow AI suggestions, whereas experts frequently overrode correct AI advice, resulting in lower overall performance, revealing a paradox of expertise in educational decision-making. We further compare two explanation modalities: textual reasoning and inline highlighting. Textual reasoning reduced under-reliance when the AI was correct but increased over-reliance when the AI was wrong, while inline highlighting exerted minimal influence on either behavior. Notably, neither explanation modality improved accuracy, and both increased time costs. As a contribution to learning analytics, we demonstrate how reliance patterns (over-reliance and under-reliance) and time cost function as process-level indicators that reveal how users integrate, or fail to integrate, AI recommendations. Our findings underscore the need for adaptive, trust-calibrated explanation strategies in tutor-facing decision support systems that balance accuracy, efficiency, and accountability in human-AI collaboration.

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The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.

Broad Al literacy is essential in today's rapidly advancing technological landscape, extending beyond Al specialists to encompass the general public. However, the complexity of Al concepts poses significant barriers to learning for individuals without prior Al knowledge. While teaching through analogies is a well-recognized method to simplify complex information by connecting it to familiar concepts, adapting these analogies to match individual learner profiles remains a substantial challenge. This paper addresses this gap by proposing a novel method for personalizing educational analogies, enhancing the accessibility and engagement of AI concepts for a diverse audience. Our approach uses Large language models (LLMs) to dynamically tailor content to each learner's cognitive and cultural contexts, grounded in educational theories and practices. Utilizing a crowdsourced AIB testing framework through Prolific (N-60), this research contrasts conventional instructional methods with content incorporating LLM-enhanced personalized analogies. Data collection comprised pre- and post-tests, activity logs, and surveys featuring Likert-scale and open-ended questions. Quantitative analysis of key learning outcomes revealed significant improvements in comprehension and retention, evidenced by enhanced pre-and post- test scores (p < 0.01 and p < 0,05, respectively) and motivation, as indicated by increased engagement in survey responses (p < 0.05). Qualitative analysis revealed a need for more examples and visual aids to complement analogies and a preference for balancing analogies with detailed technical content. This study demonstrates the potential of Al-generated analogies to make complex Al concepts more accessible and engaging. Future research should refine analogy generation. incorporate multimedia elements, and explore long-term and cross-cultural impacts to further enhance Al education.

We developed GPTutor, a pioneering web application designed to revolutionize personalized learning by leveraging the capabilities of Generative AI at scale. GPTutor adapts educational content and practice exercises to align with individual students' interests and career goals, enhancing their engagement and understanding of critical academic concepts. The system uses a serverless architecture to deliver personalized and scalable learning experiences. By integrating advanced Chain-of-Thoughts prompting methods, GPTutor provides a personalized educational journey that not only addresses the unique interests of each student but also prepares them for future professional success. This demo paper presents the design, functionality, and potential of GPTutor to foster a more engaging and effective educational environment.

Written feedback has long been a cornerstone in educational and professional settings, essential for enhancing learning outcomes. However, multimodal feedback-integrating textual, auditory, and visual cues-promises a more engaging and effective learning experience. By leveraging multiple sensory channels, multimodal feedback better accommodates diverse learning preferences and aids in deeper information retention. Despite its potential, creating multimodal feedback poses challenges, including the need for increased time and resources. Recent advancements in generative artificial intelligence (GenAI) offer solutions to automate the feedback process, predominantly focusing on textual feedback. Yet, the application of GenAI in generating multimodal feedback remains largely unexplored. Our study investigates the use of GenAI techniques to generate multimodal feedback, aiming to provide this feedback for large cohorts of learners, thereby enhancing learning experience and engagement. By exploring the potential of GenAI for this purpose, we propose a framework for automating the generation of multimodal feedback, which we name MuFIN.