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Guanze Liao

dblp:129/0776 · also Guan-Ze Liao · DBLP profile ↗
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4ranked-venue papers
0as first author
4since 2021 · last 2026
0000-0002-5758-3396ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Learning and educational technologies · 88% Design research and methods · 12%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Computing education · 100%
Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › AI education
AI literacy
1.922026
Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module · AAAI 2026
"From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education · AAAI 2025
Learning and educational technologies
k-12 education
0.912025
"From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education · AAAI 2025
Design research and methods
participatory design
0.312025
"From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education · AAAI 2025

Methods — techniques the papers use, named apart from their topics

scenario-based deliberate practice · 3.0classroom deployment study · 3.0AI-based auto-grading · 3.0project-based learning · 1.7interviews · 1.7co-design sessions · 1.7
YearPublicationVenuePosition
2026 Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module
abstract
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 a 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.
Ruiwei Xiao, Xinying Hou, Ying-Jui Tseng, Hsuan Nieu, Guanze Liao, John C. Stamper, Kenneth R. Koedinger
AAAI5
2026 How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures
Shan Zhang 0003, Ruiwei Xiao, Anthony Botelho, Guanze Liao, Thomas K. F. Chiu, John C. Stamper, Kenneth R. Koedinger
LAK4
2025 "From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education
abstract
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.
Ruiwei Xiao, Hsuan Nieu, Ying-Jui Tseng, Guanze Liao
AAAI5
2024 ActiveAI: The Effectiveness of an Interactive Tutoring System in Developing K-12 AI Literacy
Ying-Jui Tseng, Gautam Yadav, Xinying Hou, Muzhe Wu, Yun-Shuo Chou, Claire Che Chen, Chia-Chia Wu, Shi-Gang Chen, Yi-Jo Lin, Guanze Liao, Kenneth R. Koedinger
EC-TEL (1)10