explainable ai

Preparing Students for AI-Assisted Research Workflows

When students can obtain a script that runs, it can be easy for them to believe they understand the programming behind it. AI tools make this tension more visible. Students can now generate code, examples, explanations, and error-message fixes quickly, even while they are still developing basic programming fluency. Thi...

Building Bots That Teach Thinking: Using Custom AI Chatbots to Enhance Critical Thinking and Decision-Making

Generative AI is increasingly common in education, but its effect on student learning depends largely on how it is used. This interactive session will introduce practical strategies for creating custom AI chatbots that encourage students to think through problems rather than simply ask for answers. Participants will ex...

Designing Custom Chatbots for Classroom Use: Practical Examples, Benefits, and Current Limitations

Recent advances in generative AI have made it increasingly possible for individual educators to design and implement their own custom chatbots without advanced programming skills (Abe & Kaneshige, 2025). This workshop introduces practical examples of custom chatbots developed for educational purposes and discusses both...

Practical Mimicry and Modal Integration: Know-How, Large Language Models, and Human–AI Collaboration

A unified architectural theory of practical knowledge that assesses AI know-how at the appropriate system level, extends intellectualism, and grounds the normative assessment of human-AI collaboration is defended.

Explainable AI for resource-aware intervention prioritization of digital burnout in healthcare workplaces

Developing a generative AI chatbot to support students’ higher-order thinking in STEM education

A proof of concept test of whether interaction with the authors' generative artificial intelligence chatbot could elicit students’ higher-order thinking talk (HOTT) in conversation as they reflected on their solutions to proportional reasoning problems suggested that human-artificial intelligence dialog can lead to ins...

xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers

This paper proposes a technique built on LightGCN like architectures, an influential and popular class of recommendation models, that provides an appropriate assurance mechanism for these AI systems leading to specific advantages, including interpretable explanations and a theoretically guaranteed measure of explanatio...

Machine Learning for Credit Risk Modeling: A Comparative Study of Traditional & Explainable AI Approaches

A comparative study of traditional machine learning and explainable AI (XAI) approaches for credit risk modeling. The project evaluates model performance alongside interpretability, demonstrating end-to-end data science and machine learning skills for educational and portfolio purposes.

Nonuniformity in Multi‑Physical Field Drying of Fruits and Vegetables: Mechanisms, Cascade Amplification, and Artificial Intelligence–Driven Control

ABSTRACT Nonuniformity is the most critical yet unresolved bottleneck limiting the industrial adoption of multi‐physical field drying (microwave, radio frequency, infrared, and ultrasound) for fruits and vegetables. While individual physical field technologies and AI applications have been reviewed separately, a concep...

TC-Genesis-Research

Research software for global 24-hour tropical cyclogenesis screening using complementary deep-learning experts, learned ensemble fusion, Explainable AI, historical verification, candidate post-classification, and operational GeoJSON generation.

Beyond the Flag: Yearly Cybersecurity Project Ideas for Secondary Students

Beyond the Flag is a free, adaptable guide for teachers supervising long student projects in cybersecurity, in any curriculum: MEB yearly projects, IB Extended Essays, Internal Assessments and MYP Personal Projects, AP courses and capstones, the Extended Project Qualification (EPQ), and clubs. Its central principle is...

Citrus leaf pathology multi-class image dataset and a hybrid multi-branch attention fusion network for lemon leaf disease classification

Abstract The productivity of lemon (Citrus lemon) crops is severely undermined by foliar diseases, which constitute the leading cause of global harvest degradation. The conventional reliance on manual identification by experienced specialists poses a significant bottleneck, as these methods are both time-consuming and...