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From Data Trends to Privacy Insights in Mental Health Apps: an LLM-Powered Approach

Conference Session
DASI Technical Session 2: Artificial Intelligence in Higher Education
Collection
2025 ASEE Annual Conference & Exposition
Authors
Xingyu You, Arcadia University; Wang Wang, Arcadia University; Zhairui Shen; Yanxia Jia, Arcadia University
Tagged Divisions
Data Science and Artificial Intelligence (DSAI) Constituent Committee
options to opt out or delete their data.The LLM- and RAG-based analysis demonstrates high accuracy and reliability in answeringprivacy-related Yes/No APA questions, outperforming methods solely based on promptengineering by grounding responses in relevant policy text. This approach offers a promisingsolution for automated privacy policy analysis, helping users, developers, and policymakersassess data practices more effectively.Beyond its technical contributions, this study was conducted as part of an undergraduate researchproject, where student researchers actively participated in research problem identification, dataanalysis, and performance evaluation. The project provided hands-on experience in privacypolicy analysis using LLMs and RAG
Relevance Score: 0.0041712984

Shaping Future Innovators: A Curriculum Comparison of Data Science Programs in Leading U.S. and Chinese Institutions

Conference Session
DSAI Technical Session 3: Integrating Data Science in Curriculum Design
Collection
2025 ASEE Annual Conference & Exposition
Authors
Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University; Duo Li, Shenyang Institute of Technology
Tagged Topics
Diversity
Tagged Divisions
Data Science and Artificial Intelligence (DSAI) Constituent Committee
U.S. course syllabi were examined, and content analysis was conducted usingcourse titles, course description, weekly topics, homework assignments, project descriptions, andclasswork. Similarly, a total of 42 Chinese course syllabi were examined and content analysiswas conducted using course title, course description, and course topics.Three domain experts developed codes based on ACM curricular framework. They coded thesample syllabus data, achieving an acceptable inter-coder reliability with over 85% agreement.4. Results4.1 Comparison of Data Science Knowledge and Skill in Core CurriculumBased on the framework for data science knowledge/skill in Table IV, we coded course topicsfor all 82 courses. Table IX lists the total number of course
Relevance Score: 0.0041712984

The Hidden Costs of Complexity: Using Causal Inference and Double Machine Learning to Uncover Important Relationships in Higher Education Data Sets

Conference Session
DSAI Technical Session 4: Workshops, Professional Development, and Training
Collection
2025 ASEE Annual Conference & Exposition
Authors
Melika Akbarsharifi, The University of Arizona; Ahmad Slim; Gregory L. Heileman, The University of Arizona; Roxana Akbarsharifi, The University of Arizona; Kristina A Manasil, The University of Arizona
Tagged Topics
Diversity
Tagged Divisions
Data Science and Artificial Intelligence (DSAI) Constituent Committee
U.S., collected between 2000 and2022 as part of an Ascendium Foundation research project. Each institution provided anonymizedstudent-level data, including demographic information, academic performance metrics, and cur-ricular complexity measures. Table 2.1 summarizes the participating universities and the numberof programs and students contributed by each institution. 3 University Number of Pro- Number of Students grams University of Arizona 175 47410 Colorado State University 114 34471 Florida
Relevance Score: 0.0041712984

The role of professional development in enhancing AI literacy competence development and participation in the Intelligence Age

Conference Session
DASI Technical Session 2: Artificial Intelligence in Higher Education
Collection
2025 ASEE Annual Conference & Exposition
Authors
Ibukun Samuel Osunbunmi, Pennsylvania State University; Taiwo Raphael Feyijimi, University of Georgia; Lexy Chiwete Arinze, Purdue University at West Lafayette (COE); Viyon Dansu, Florida International University; Bolaji Ruth Bamidele, Utah State University; Yashin Brijmohan, Utah State University; Stephanie Cutler, The Pennsylvania State University
Tagged Topics
Diversity
Tagged Divisions
Data Science and Artificial Intelligence (DSAI) Constituent Committee
Large Language Models (LLMs). Taiwo is known for his ability to collaborate effectively within and across organizations to meet project goals and drive transformative results. He excels in leading technical teams, offering strategic IT consultations, and implementing solutions that enhance productivity.Lexy Chiwete Arinze, Purdue University at West Lafayette (COE) Lexy Arinze is a first-generation PhD student in the School of Engineering Education at Purdue University and a Graduate Research Assistant with the Global Learning Initiatives for the Development of Engineers (GLIDE) research group. Lexy’s research interests include early career engineers, Artificial Intelligence, experiential learning, and global
Relevance Score: 0.0035753986

Can AI Transform Graduate Computer Science Admissions? Preemptive Bias Detection in Automated Review Systems

Conference Session
DASI Technical Session 2: Artificial Intelligence in Higher Education
Collection
2025 ASEE Annual Conference & Exposition
Authors
Ananya Prakash, Virginia Polytechnic Institute and State University; Mohammed Seyam, Virginia Polytechnic Institute and State University
Tagged Topics
Diversity
Tagged Divisions
Data Science and Artificial Intelligence (DSAI) Constituent Committee
Engineering, Human-Computer Interaction, and Computer Science Education. Additionally, he is the CS Department Coordinator for Experiential Learning, where he leads several initiatives to enhance students’ learning through out-of-classroom experiences, including the CS Study Abroad program. Mohammed has 20+ years of experience in teaching university level courses, and he presented and conducted multiple talks and workshops in different countries. Among other courses, he taught: Software Engineering, Database Systems, Usability Engineering, and Software Project Management. ©American Society for Engineering Education, 2025 Can AI Transform Graduate Computer Science Admissions
Relevance Score: 0.0029794988

PEER HELPER (Peer Engagement for Effective Reflection, Holistic Engineering Learning, Planning, and Encouraging Reflection) Automated Discourse Analysis Framework

Conference Session
DSAI Technical Session 4: Workshops, Professional Development, and Training
Collection
2025 ASEE Annual Conference & Exposition
Authors
yilin zhang, University of Florida; Bruce F. Carroll, University of Florida; Jinnie Shin, University of Florida; Kent J. Crippen, University of Florida
Tagged Divisions
Data Science and Artificial Intelligence (DSAI) Constituent Committee
on it. Exploration and Dialogue promoting • Open-ended questions M: How does this align Reflection deeper thinking about • Self-assessment with goals? experiences and • Reflective discussions S: Need more practical decisions. projects. Note: M = Mentor; S = Student/Mentee. Examples are abbreviated for space.2.5.1 Framework Development and ValidationThe adaptation of this framework for peer mentoring contexts involved several keyconsiderations including category flexibility, where statements may exhibit characteristics ofmultiple categories; response dynamics, where
Relevance Score: 0.0029794988