ADAPTIVE INTERFACES | AI-AUGMENTATION | PERSONALIZATION | UNIVERSITY WEBSITE | USER EXPERIENCE

AUGMENTING UNIVERSITY WEB EXPERIENCES WITH AI

[KIIE AUTUMN 2025 (UNDER REVIEW)] EXPLORING AI-AUGMENTED PERSONALIZATION:
AN INTERVIEW-BASED STUDY ON ENHANCING UNIVERSITY WEB EXPERIENCES

ROLE

Qualitative Interviewer

DESCRIPTION

As a TA for Professor Keeheon Lee, the Director of the Humanities, Arts, and Social Sciences Division at Yonsei University, I conducted the user research process while planning to redesign the division's official website. As a UX researcher, I conducted a qualitative interview study to explore the possibility of developing an adaptive, AI-powered interface that could personalize university website experiences.

ADVISOR

Keeheon Lee

TIMELINE

Sep 2025 - Oct 2025

RESEARCH METHODS

Semi-structured interviews

TARGET POPULATION

Undergraduate students (Age 18-29)
in University

PROJECT INTRODUCTION

To address the lack of personalization of university websites and to explore the possibility of augmenting websites with AI, this study investigates what experiences and needs users have regarding such websites.

PROBLEM STATEMENT

University website users are often confused
due to disorganized navigation and content

Current university websites suffer from generic, one-size-fits-all designs that lack personalization, fail to accommodate diverse user needs, and present disjointed navigation structures, leading to inefficient information retrieval, lower user satisfaction, and diminished institutional engagement.

University Website Usability Issues

Current studies identify major usability limitations of university websites, which lead to inefficient information retrieval and reduced user satisfaction.

Examples of limitations:

lack of personalization
failure to reflect diverse user needs
one-way information delivery,
disjointed menu structure and navigation
accessibility barriers

There is a need for user-centered design improvements.

Abdi, S. J., & Greenacre, Z. A. (2020). An approach to website design for Turkish universities, based on the emotional responses of students. Cogent Engineering, 7(1), 1–14. https://doi.org/10.1080/23311916.2020.1770915

Albaghli, Z., Rahman, M., & Ahmed, S. (2024). Usability of university websites from students' perspective: A mixed‐methods evaluation. International Journal of Information Systems, 28(3), 120–138.

Atikuzzaman, Md. (2025). Evaluating information access and usability of a university website: A mixed-method study on students, experts and authority. Information Discovery and Delivery. https://doi.org/10.1108/IDD-11-2024-0177

Bautista, R. (2020). Students' perspectives on university web site usability. Journal of Web Usability, 15(2), 45–62.

Karani, A., Thanki, H., & Achuthan, S. (2021). Impact of university website usability on satisfaction: A structural equation modelling approach. Management and Labour Studies, 46, 1–18. https://doi.org/10.1177/0258042X21989924

Yangiboyev, S., Nasimov, B., Turaeva, D., & Ermatov, T. (2023). Analysis of the impact of a university website on attracting and retaining students using the DEMATEL method. In Proceedings of the 7th International Conference on Future Networks and Distributed Systems (pp. 298–303). https://doi.org/10.1145/3644713.3644752

AI-Driven Personalization

Research on AI-augmented systems demonstrates that adaptive personalization techniques can enhance information search efficiency, boost user engagement and satisfaction, and strengthen institutional credibility and brand image through context-aware content delivery.

Google's "Learn Your Way" is a precedent for using AI-driven personalization.

Bhuiyan, M. (2024). The role of AI‐enhanced personalization in customer experience. Journal of AI Marketing, 5(1), 22–35.

Blömker, L., & Schmidt, K. (2025). Evaluating personalization in AI‐powered service chatbots: Persona‐based immersion study. Service Science Review, 12(2), 98–113.

Hardcastle, K., Vorster, L., & Brown, D. M. (2025). Understanding customer responses to AI-driven personalized journeys: Impacts on the customer experience. Journal of Advertising, 54(2), 176–195. https://doi.org/10.1080/00913367.2025.2460985

Martín, A., et al. (2025). Towards an AI-augmented textbook. arXiv preprint arXiv:2509.13348.

Patil, S. (2024). AI‐driven customer service: Mechanisms for personalization, loyalty, and satisfaction. Journal of Service Management, 17(4), 56–70.

Zanker, M., Rook, L., & Jannach, D. (2019). Measuring the impact of online personalisation: Past, present and future. International Journal of Human-Computer Studies, 131, 160–168. https://doi.org/10.1016/j.ijhcs.2019.06.006

User Segmentation & Personas

Current literature emphasizes the use of user segmentation and persona development to capture distinct user behaviors, goals, and preferences. This indicates a need for more targeted design of personalized interfaces that address the needs of novice versus expert users, domestic versus international students, and specialized academic interests.

Han, O. (2023). A study on components for designing personalized education systems based on generative AI. The Journal of Korean Association of Computer Education, 26(6), 127–141.

Interaction Design Foundation (IxDF). (2025). Personas – why and how you should use them. Interaction Design Foundation Guide.

Kim, S., & Cho, M. K. (2022). AI-based educational platform analysis supporting personalized mathematics learning. Journal of Mathematics Education, 36(3), 417–438.

Nielsen, P., & Landauer, T. K. (1993). A mathematical model of the finding of usability problems. In Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems (pp. 206–213). https://doi.org/10.1145/169059.169166

Sharp, A. (2025). Personas – a simple introduction. Interaction Design Journal, 8(1), 10–25.

Shin, M., & Kim, B. (2006). A study on the possibility of user classification by web-using types. Archives of Design Research, 19(1), 317–328.

UK Statistics Authority. (2024). Understanding your users: User personas – Content style guide (Technical Report). UKSA.

Accessibility, Privacy,
and Ethics

Prior work highlights persistent accessibility challenges on higher-ed websites, particularly for international students and users with diverse abilities, highlighting the need for inclusive design practices and compliance with accessibility standards.

Ethical frameworks and privacy studies stress the importance of transparency, data governance, and regulatory compliance (e.g., GDPR, FERPA) when implementing AI-driven personalization to maintain user trust and protect sensitive information.

Campoverde-Molina, M., Luján-Mora, S., & Valverde, L. (2021). Accessibility of university websites worldwide: A systematic literature review. Universal Access in the Information Society, 22(1), 133–168.

Harper, T., & van den Broek, J. (2025). Digital accessibility in higher education: A Dutch case study. ACM Digital Library, 10(4), 215–228.

Raduenzel, B. (2024). Web accessibility in higher education (2nd ed.). Konabos Blog.

RESEARCH QUESTION

RQ1

Current user experiences & pain points with university websites

RQ1.1: What difficulties do users face?
RQ1.2: What features are most frequently used?
RQ1.3: How do usage patterns differ by user type?
RQ1.4: What improvements do users need?

RQ2

How users perceive AI-augmented personalization

RQ2.1: What are attitudes toward AI personalization?
RQ2.2: What concerns exist?

RQ3

Types of design considerations needed

RQ3.1: What personalization features are needed?
RQ3.2: What are privacy/ethical considerations?

METHODOLOGY

STUDY DESIGN

Semi-structured qualitative interviews

PARTICIPANTS

N = 15 undergraduate students (ages 18-30)
Demographics: 8 Korean, 7 international students; 6 double majors
Distribution: 2 freshmen, 4 sophomores, 3 juniors, 6 seniors

INTERVIEW PROCESS & ANALYSIS

Interview

Interview Duration: 60 minutes each
Question Categories: Current website experiences, AI personalization perceptions, design preferences
Recording: Audio recordings with transcription

Analysis

Thematic analysis using coding techniques
Categories: pain points, personalization opportunities, design considerations

RESULTS

I was able to interview a total of fifteen undergraduate students regarding the difficulties related to their experiences of using university websites.

CURRENT WEBSITE PAIN POINTS

Navigation Complexity

(KOR ENG Translated) “The website structure is confusing, with menus and pages laid out in a way that doesn’t make sense, so I often get lost trying to find what I need.”

Lack of Personalization

(KOR ENG Translated) “I don't really check university websites because I feel like the information isn't really relevant to me.”

Accessibility Issues

"As an international student, I struggle with language barriers on the website. Navigating around can be very difficult. Even if I use Google Translate, the translations do not make any sense, or some are not even translated at all."

PERCEPTIONS OF AI PERSONALIZATION

Positive Attitudes

Users appreciate efficiency gains, similar to ChatGPT experiences

Privacy Concerns

Worries about data collection and usage

DESIRED DESIGN FEATURES

Personalized Content

Major-specific information, course recommendations, academic calendar

Interactive Elements

AI chatbots for instant support, customizable dashboards

DISCUSSION

User Personas

After conducting user interviews, I came up with four different user personas based on their characteristics and needs.

The Quick Information-Seeker

Characteristics:

Goal-oriented, wants quick answers

Needs:

Efficient search, shortcuts

Design Implications:

AI chatbot, quick-access menu

Design Guidelines

Balance personalization with user control

Accommodate diverse user needs

Ensure transparency

in AI decision-making

Maintain privacy safeguards

Next Step

In order to redesign the website, I plan on conducting research on other stakeholder groups, other than just undergraduate students. I am currently thinking of doing user research with faculty, parents, and researchers outside of Underwood International College.

Impact

This study extends research about AI-personalization beyond commercial e-commerce contexts into educational settings, where users have different motivations, trust concerns, and information needs. It reveals that students are cautiously optimistic about AI personalization but require transparency about data usage and meaningful control over their experiences. By documenting how generic university website interfaces fail diverse user groups, this research emphasizes inclusive design practices that could incorporate AI to adapt to individual needs rather than expecting users to adapt to rigid systems.