arXiv subjects

Ruiwei Xiao

Publications and source records attributed to Ruiwei Xiao.

17 recordsLinked to original sources

arXiv2026

PromptDecipher: Supporting AI Tutor Authoring Through Editable Simulated Interactions

Chatbots have long been explored as tools to support learning, and recent advances in large language models have significantly expanded the availability of platforms for educators to author AI tutoring chatbots. Yet effective authorship demands more than writing a system prompt; it requires educators to act as learning designers, AI interaction designers, and QA engineers. In practice, however, teachers rarely fulfill these roles. Our formative study found that virtually none systematically tested their bots before deploying them to students. To address this gap, we present PromptDecipher, a system that restructures the authoring workflow around a direct correction-based interaction rather than writing abstract system prompts, teachers interact with a live chat preview and edit undesirable bot responses. An automated pipeline then analyzes the correction, proposes a targeted system prompt rewrite, and validates the change across pre-defined test scenarios. This enforces QA as a first-class activity and scaffolds teachers in roles they would otherwise skip. PromptDecipher will be deployed in an AI for Educators course enrolling hundreds of higher-education instructors. A live prototype (https://teacher-prompting.vercel.app/), an anonymized codebase (https://anonymous.4open.science/r/teacher-prompting-2EDF/), and anonymized demo (https://tinyurl.com/las-prompt-decipher-demo) are available via links in the footnote.

cs.HC↗

arXiv2026

The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube

The digital transformation of religious practice has reshaped how billions of people engage with spiritual content, with video-sharing platforms becoming central to contemporary religious communication. Yet HCI research lacks systematic understanding of how narrative and visual elements create meaningful spiritual experiences and foster viewer engagement. We present a mixed-methods study of religious videos on YouTube across major religions, developing taxonomies of narrative frameworks, visual elements, and viewer interaction. Using LLM-assisted analysis, we studied relationships between content characteristics and viewer responses. Religious videos predominantly adopt lecture-style formats with authority-based persuasion strategies, using salvation narratives for guidance. All prefer bright lighting, with Buddhism favoring warm tones and prominent symbols, Judaism preferring indoor settings, and Hinduism emphasizing sacred objects. We identified differentiated patterns of emotional sharing among religious viewers while revealing significant correlations between content characteristics and engagement, particularly regarding AI-generated content. We provide evidence-based guidance for creating inclusive and engaging spiritual media.

cs.HC↗

arXiv2026

Transforming GenAI Policy to Prompting Instruction: An RCT of Scalable Prompting Interventions in a CS1 Course

Despite universal GenAI adoption, students cannot distinguish task performance from actual learning and lack skills to leverage AI for learning, leading to worse exam performance when AI use remains unreflective. Yet few interventions teaching students to prompt AI as a tutor rather than solution provider have been validated at scale through randomized controlled trials (RCTs). To bridge this gap, we conducted a semester-long RCT (N=979) with four ICAP framework-based instructional conditions varying in engagement intensity with a pre-test, immediate and delayed post-test and surveys. Mixed methods analysis results showed: (1) All conditions significantly improved prompting skills, with gains increasing progressively from Condition 1 to Condition 4, validating ICAP's cognitive engagement hierarchy; (2) for students with similar pre-test scores, higher learning gain in immediate post-test predict higher final exam score, though no direct between-group differences emerged; (3) Our interventions are suitable and scalable solutions for diverse educational contexts, resources and learners. Together, this study makes empirical and theoretical contributions: (1) theoretically, we provided one of the first large-scale RCTs examining how cognitive engagement shapes learning in prompting literacy and clarifying the relationship between learning-oriented prompting skills and broader academic performance; (2) empirically, we offered timely design guidance for transforming GenAI classroom policies into scalable, actionable prompting literacy instruction to advance learning in the era of Generative AI.

cs.HC↗

arXiv2026

Do Teachers Dream of GenAI Widening Educational (In)equality? Envisioning the Future of K-12 GenAI Education from Global Teachers' Perspectives

Generative artificial intelligence (GenAI) is rapidly entering K-12 classrooms worldwide, initiating urgent debates about its potential to either reduce or exacerbate educational inequalities. Drawing on interviews with 30 K-12 teachers across the United States, South Africa, and Taiwan, this study examines how teachers navigate this GenAI tension around educational equalities. We found teachers actively framed GenAI education as an equality-oriented practice: they used it to alleviate pre-existing inequalities while simultaneously working to prevent new inequalities from emerging. Despite these efforts, teachers confronted persistent systemic barriers, i.e., unequal infrastructure, insufficient professional training, and restrictive social norms, that individual initiative alone could not overcome. Teachers thus articulated normative visions for more inclusive GenAI education. By centering teachers' practices, constraints, and future envisions, this study contributes a global account of how GenAI education is being integrated into K-12 contexts and highlights what is required to make its adoption genuinely equal.

cs.HC↗

arXiv2026

How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures

The widespread adoption of Artificial Intelligence (AI) in K-12 education highlights the need for psychometrically-tested measures of teachers' AI literacy. Existing work has primarily relied on either self-report (SR) or objective-based (OB) assessments, with few studies aligning the two within a shared framework to compare perceived versus demonstrated competencies or examine how prior AI literacy experience shapes this relationship. This gap limits the scalability of learning analytics and the development of learner profile-driven instructional design. In this study, we developed and evaluated SR and OB measures of teacher AI literacy within the established framework of Concept, Use, Evaluate, and Ethics. Confirmatory factor analyses support construct validity with good reliability and acceptable fit. Results reveal a low correlation between SR and OB factors. Latent profile analysis identified six distinct profiles, including overestimation (SR > OB), underestimation (SR < OB), alignment (SR close to OB), and a unique low-SR/low-OB profile among teachers without AI literacy experience. Theoretically, this work extends existing AI literacy frameworks by validating SR and OB measures on shared dimensions. Practically, the instruments function as diagnostic tools for professional development, supporting AI-informed decisions (e.g., growth monitoring, needs profiling) and enabling scalable learning analytics interventions tailored to teacher subgroups.

cs.CY↗

arXiv2025

Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module

As Artificial Intelligence (AI) becomes increasingly integrated into daily life, there is a growing need to equip the next generation with the ability to apply, interact with, evaluate, and collaborate with AI systems responsibly. Prior research highlights the urgent demand from K-12 educators to teach students the ethical and effective use of AI for learning. To address this need, we designed an Large-Language Model (LLM)-based module to teach prompting literacy. This includes scenario-based deliberate practice activities with direct interaction with intelligent LLM agents, aiming to foster secondary school students' responsible engagement with AI chatbots. We conducted two iterations of classroom deployment in 11 authentic secondary education classrooms, and evaluated 1) AI-based auto-grader's capability; 2) students' prompting performance and confidence changes towards using AI for learning; and 3) the quality of learning and assessment materials. Results indicated that the AI-based auto-grader could grade student-written prompts with satisfactory quality. In addition, the instructional materials supported students in improving their prompting skills through practice and led to positive shifts in their perceptions of using AI for learning. Furthermore, data from Study 1 informed assessment revisions in Study 2. Analyses of item difficulty and discrimination in Study 2 showed that True/False and open-ended questions could measure prompting literacy more effectively than multiple-choice questions for our target learners. These promising outcomes highlight the potential for broader deployment and highlight the need for broader studies to assess learning effectiveness and assessment design.

cs.HC↗

arXiv2025

Exploring Student Choice and the Use of Multimodal Generative AI in Programming Learning

The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun supporting multiple modes of communication, known as multimodality. In this work, we explored how undergraduate programming novices choose and work with multimodal GenAI tools, and their criteria for choices. We selected a commercially available multimodal GenAI platform for interaction, as it supports multiple input and output modalities, including text, audio, image upload, and real-time screen-sharing. Through 16 think-aloud sessions that combined participant observation with follow-up semi-structured interviews, we investigated student modality choices for GenAI tools when completing programming problems and the underlying criteria for modality selections. With multimodal communication emerging as the future of AI in education, this work aims to spark continued exploration on understanding student interaction with multimodal GenAI in the context of CS education.

cs.HC↗

arXiv2025

Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices

Generative AI (GenAI) is rapidly entering K-12 classrooms, offering teachers new ways for teaching practices. Yet GenAI models are often trained on culturally uneven datasets, embedding a "default culture" that often misaligns with local classrooms. To understand how teachers navigate this gap, we defined the new concept Cultural Distance (the gap between GenAI's default cultural repertoire and the situated demands of teaching practice) and conducted in-depth interviews with 30 K-12 teachers, 10 each from South Africa, Taiwan, and the United States, who had integrated AI into their teaching practice. These teachers' experiences informed the development of our three-level cultural distance framework. This work contributes the concept and framework of cultural distance, six illustrative instances spanning in low, mid, high distance levels with teachers' experiences and strategies for addressing them. Empirically, we offer implications to help AI designers, policymakers, and educators create more equitable and culturally responsive GenAI tools for education.

cs.HC↗

arXiv2025

Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design

K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can bridge this gap, most teachers lack the time or expertise and find it difficult to encode such pedagogical nuance into their requests. This study shifts pedagogical expertise from the user's prompt to the LLM's internal architecture. We embed the well-established Knowledge-Learning-Instruction (KLI) framework into a Multi-Agent System (MAS) to act as a sophisticated instructional designer. We tested three systems for generating secondary Math and Science learning activities: a Single-Agent baseline simulating typical teacher prompts; a role-based MAS where agents work sequentially; and a collaborative MAS-CMD where agents co-construct activities through conquer and merge discussion. The generated materials were evaluated by 20 practicing teachers and a complementary LLM-as-a-judge system using the Quality Matters (QM) K-12 standards. While the rubric scores showed only small, often statistically insignificant differences between the systems, the qualitative feedback from educators painted a clear and compelling picture. Teachers strongly preferred the activities from the collaborative MAS-CMD, describing them as significantly more creative, contextually relevant, and classroom-ready. Our findings show that embedding pedagogical principles into LLM systems offers a scalable path for creating high-quality educational content.

cs.CY↗

arXiv2025

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education

With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes. This work aims to teach students how to effectively prompt LLMs to improve their learning. We first proposed pedagogical prompting, a theoretically-grounded new concept to elicit learning-oriented responses from LLMs. To move from concept design to a proof-of-concept learning intervention in real educational settings, we selected early undergraduate CS education (CS1/CS2) as the example context. We began with a formative survey study with instructors (N=36) teaching early-stage undergraduate-level CS courses to inform the instructional design based on classroom needs. Based on their insights, we designed and developed a learning intervention through an interactive system with scenario-based instruction to train pedagogical prompting skills. Finally, we evaluated its instructional effectiveness through a user study with CS novice students (N=22) using pre/post-tests. Through mixed methods analyses, our results indicate significant improvements in learners' LLM-based pedagogical help-seeking skills, along with positive attitudes toward the system and increased willingness to use pedagogical prompts in the future. Our contributions include (1) a theoretical framework of pedagogical prompting; (2) empirical insights into current instructor attitudes toward pedagogical prompting; and (3) a learning intervention design with an interactive learning tool and scenario-based instruction leading to promising results on teaching LLM-based help-seeking. Our approach is scalable for broader implementation in classrooms and has the potential to be integrated into tools like ChatGPT as an on-boarding experience to encourage learning-oriented use of generative AI.

cs.HC↗

arXiv2025

LLMs to Support K-12 Teachers in Culturally Relevant Pedagogy: An AI Literacy Example

Culturally Relevant Pedagogy (CRP) is vital in K-12 education, yet teachers struggle to implement CRP into practice due to time, training, and resource gaps. This study explores how Large Language Models (LLMs) can address these barriers by introducing CulturAIEd, an LLM tool that assists teachers in adapting AI literacy curricula to students' cultural contexts. Through an exploratory pilot with four K-12 teachers, we examined CulturAIEd's impact on CRP integration. Results showed CulturAIEd enhanced teachers' confidence in identifying opportunities for cultural responsiveness in learning activities and making culturally responsive modifications to existing activities. They valued CulturAIEd's streamlined integration of student demographic information, immediate actionable feedback, which could result in high implementation efficiency. This exploration of teacher-AI collaboration highlights how LLM can help teachers include CRP components into their instructional practices efficiently, especially in global priorities for future-ready education, such as AI literacy.

cs.CY↗

arXiv2024

"From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-based Learning Toolkit in K-12 Education

As artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively. Through interviews and co-design sessions with K-12 teachers, we examined current AI literacy levels and how teachers adapt AI tools like the AI Art Lab, AI Music Studio, and AI Chatbot into their course designs. While teachers appreciated the potential of AI tools to foster creativity and critical thinking, they also expressed concerns about the accuracy, trustworthiness, and ethical implications of AI-generated content. Our findings reveal the challenges teachers face, including limited resources, varying student and instructor skill levels, and the need for scalable, adaptable AI tools. This research contributes insights that can inform the development of AI curricula tailored to diverse educational contexts.

cs.AI↗

arXiv2024

ActiveAI: Enabling K-12 AI Literacy Education & Analytics at Scale

Interest in K-12 AI Literacy education has surged in the past year, yet large-scale learning data remains scarce despite considerable efforts in developing learning materials and running summer programs. To make larger scale dataset available and enable more replicable findings, we developed an intelligent online learning platform featuring AI Literacy modules and assessments, engaging 1,000 users from 12 secondary schools. Preliminary analysis of the data reveals patterns in prior knowledge levels of AI Literacy, gender differences in assessment scores, and the effectiveness of instructional activities. With open access to this de-identified dataset, researchers can perform secondary analyses, advancing the understanding in this emerging field of AI Literacy education.

cs.HC↗

arXiv2024

Generating AI Literacy MCQs: A Multi-Agent LLM Approach

Artificial intelligence (AI) is transforming society, making it crucial to prepare the next generation through AI literacy in K-12 education. However, scalable and reliable AI literacy materials and assessment resources are lacking. To address this gap, our study presents a novel approach to generating multiple-choice questions (MCQs) for AI literacy assessments. Our method utilizes large language models (LLMs) to automatically generate scalable, high-quality assessment questions. These questions align with user-provided learning objectives, grade levels, and Bloom's Taxonomy levels. We introduce an iterative workflow incorporating LLM-powered critique agents to ensure the generated questions meet pedagogical standards. In the preliminary evaluation, experts expressed strong interest in using the LLM-generated MCQs, indicating that this system could enrich existing AI literacy materials and provide a valuable addition to the toolkit of K-12 educators.

cs.HC↗

arXiv2024

Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms

Self-reflection on learning experiences constitutes a fundamental cognitive process, essential for the consolidation of knowledge and the enhancement of learning efficacy. However, traditional methods to facilitate reflection often face challenges in personalization, immediacy of feedback, engagement, and scalability. Integration of Large Language Models (LLMs) into the reflection process could mitigate these limitations. In this paper, we conducted two randomized field experiments in undergraduate computer science courses to investigate the potential of LLMs to help students engage in post-lesson reflection. In the first experiment (N=145), students completed a take-home assignment with the support of an LLM assistant; half of these students were then provided access to an LLM designed to facilitate self-reflection. The results indicated that the students assigned to LLM-guided reflection reported increased self-confidence and performed better on a subsequent exam two weeks later than their peers in the control condition. In the second experiment (N=112), we evaluated the impact of LLM-guided self-reflection against other scalable reflection methods, such as questionnaire-based activities and review of key lecture slides, after assignment. Our findings suggest that the students in the questionnaire and LLM-based reflection groups performed equally well and better than those who were only exposed to lecture slides, according to their scores on a proctored exam two weeks later on the same subject matter. These results underscore the utility of LLM-guided reflection and questionnaire-based activities in improving learning outcomes. Our work highlights that focusing solely on the accuracy of LLMs can overlook their potential to enhance metacognitive skills through practices such as self-reflection. We discuss the implications of our research for the Edtech community.

cs.CY↗

arXiv2024

Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences

The field of Artificial Intelligence in Education (AIED) focuses on the intersection of technology, education, and psychology, placing a strong emphasis on supporting learners' needs with compassion and understanding. The growing prominence of Large Language Models (LLMs) has led to the development of scalable solutions within educational settings, including generating different types of feedback in Intelligent Tutoring Systems. However, the approach to utilizing these models often involves directly formulating prompts to solicit specific information, lacking a solid theoretical foundation for prompt construction and empirical assessments of their impact on learning. This work advocates careful and caring AIED research by going through previous research on feedback generation in ITS, with emphasis on the theoretical frameworks they utilized and the efficacy of the corresponding design in empirical evaluations, and then suggesting opportunities to apply these evidence-based principles to the design, experiment, and evaluation phases of LLM-based feedback generation. The main contributions of this paper include: an avocation of applying more cautious, theoretically grounded methods in feedback generation in the era of generative AI; and practical suggestions on theory and evidence-based feedback design for LLM-powered ITS.

cs.HC↗

arXiv2024

Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices

Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving. However, most existing LLM-based hint systems are limited to one single hint type. To investigate whether and how different levels of hints can support students' problem-solving and learning, we conducted a think-aloud study with 12 novices using the LLM Hint Factory, a system providing four levels of hints from general natural language guidance to concrete code assistance, varying in format and granularity. We discovered that high-level natural language hints alone can be helpless or even misleading, especially when addressing next-step or syntax-related help requests. Adding lower-level hints, like code examples with in-line comments, can better support students. The findings open up future work on customizing help responses from content, format, and granularity levels to accurately identify and meet students' learning needs.

cs.HC↗