Research Projects

See below for more information about all of the Summer 2025 projects.

Project 1 - Active Learning in STEM Education
Project 2 - AI-Assisted Conflict Resolution for Policy Changes
Project 3 - Auditing AI 3D Object Datasets
Project 4 - Codesigning AI Dataset Exploration Websites
Project 5 - Codespec: A Computer Programming Environment
Project 6 - Comprehensive Personalized Programming Practice Environment
Project 7 - Connecting Through Brick Clubs
Project 8 - Designing Algorithms for Adaptive Extended Reality (XR)
Project 9 - Designing Context-aware AI-driven Assistants to Help Patients
Project 10 - Enhancing Personalization and Accessibility in Virtual Reality Experiences
Project 11 - Finding Where all the Educational Games Have Gone
Project 12 - Fostering Empathy Through Gaming While Ridesharing
Project 13 - Human-Centered Data Science and Visualization
Project 14 - Intelligent Agents for Security
Project 15 - Interview Study with Industry Privacy Practitioners Who Engage in Responsible AI Work
Project 16 - Mental Health Mobile Game  
Project 17 - Power and Battery Free Interactive Objects Made from Paper
Project 18 - Restoring Civic Participation Through AI-enabled Reporting
Project 19 - Social Cybersecurity: Applying Social Psychology to Improve Cybersecurity
Project 20 - Supporting Middle School Math Homework with AI through Goal Setting and Experiments
Project 21 - tronicsBot: An AI Partner for Creative Electronics Design and Development
Project 22 - Visualizing AI for Treatment Decisions in Healthcare
Project 23 - VTutor
Project 24 - Workplace Surveillance Tracker
Project 25 - Advancing Mental Health Training with Realistic Virtual Patients

 

Project 1
Active Learning in STEM Education

Mentor: Paulo Carvalho, faculty

Description: Mastering material in STEM classes requires students to learn and memorize large amounts of new knowledge in a short period of time. One way that has long been argued to improve such learning is by having students practice new knowledge by spacing questions over time (spaced retrieval practice). However, the evidence for the benefits of spaced retrieval practice in STEM contexts is limited. How can learn by doing be optimized in STEM classes and what computational algorithms best capture this learning?

Nature of Student Involvement: Students will be involved in all steps of experimental research, data analytics, and student modeling.

Skills we are interested in (not all required):

• Quantitative data analyses
• Experience with experimental design and data collection
• Data science/learning analytics/student modeling experience preferred but not required

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Project 2 
AI-Assisted Conflict Resolution for Policy Changes

Mentor: Tzu-Sheng Kuo, graduate student advised by faculty Haiyi Zhu and Ken Holstein

Description: Communities often rely on regulatory policies for governance. For instance, online communities like Reddit have content moderation rules that determine what can be posted in each subreddit. However, community members may interpret these rules differently or hold conflicting views on how they should be enforced, leading to inconsistent decision-making and frustration. In this project, we are developing an AI-assisted, end-to-end system that enables community members to collaboratively identify the root causes of their disagreements and iteratively revise their communal rules to resolve these conflicts. The student will build on our previous work to develop an end-to-end system and conduct user studies within real-world community contexts.

Nature of Student Involvement: The student will implement the full-stack system, conduct the user study, and potentially write and submit a paper to top-tier HCI conferences.

Skills we are interested in (not all required):

• The student should have experience in full-stack web development (e.g., Svelte, Firebase) and a background in NLP (e.g., fine-tuning BERT models). 
• Experience with running user studies or crowdsourcing studies (e.g., using Amazon Mechanical Turk) is also a plus. 

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Project 3 
Auditing AI 3D Object Datasets

Mentor: William Agnew, Carnegie Bosch postdoctoral fellow

Description: Recently, there has been a surge of interest in AI for generating 3D objects, driven by successes in text-to-image AI. Like image generative AI, 3D object generative AI requires massive datasets of 3D objects. These datasets are likely being scraped from online repositories of 3D models and other sources. In audio, text, and visual domains, dataset practices have caused numerous ethical issues, including intellectual property theft, economic harms, bias, toxicity, CSAM, NCII, and misrepresentation. In this project, we conduct the first audit of 3D AI dataset contents and practices to understand what is in these datasets and how they are being created. This audit will help anticipate and mitigate harm with 3D AI trained on large datasets.

Nature of Student Involvement: REU students will conduct a literature review to identify popular 3D object datasets, and conduct qualitative and quantitative analyses in partnership with other collaborators to assess their contents.

Skills we are interested in (not all required):

• Competency with python
• Experience with lit reviews, audits, and qualitative or quantitative methods a plus

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Project 4
Codesigning AI Dataset Exploration Websites

Mentor: William Agnew, Carnegie Bosch postdoctoral fellow

Description: The contents of datasets used to create AI have emerged as key considerations in ethical, legal, and policy considerations. In audio, text, and image modalities, multiple lawsuits have been filed by people who are in these datasets. These lawsuits and other policy discussions will have major impacts on who profits from AI, and what rights people in AI datasets have. Key to these legal battles have been websites that enable people to easily search through large AI datasets and find if they are in them, or explore other aspects, such as bias and toxicity. However, these websites remain ad-hoc, and little about how they are used is known. In this project, we will conduct user studies and codesign on an existing website for exploring the contents of audio datasets. This study will uncover what questions people have about these datasets, how effective current data exploration websites are at answering them, and develop new features to enable the public to better understand these key components of AI.

Nature of Student Involvement: Students will conduct design and update of dataset exploration website features, and conduct and analyze interviews with people using the website.

Skills we are interested in (not all required):

• HTML, Javascript, Node.js competency are required
• Previous experience with interviews, surveys, or qualitative methods helpful
   

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Project 5
Codespec: A Computer Programming Environment

Mentor: Carl Haynes-Magyar, presidential postdoctoral fellow

Description: Despite the potential of Parsons problems, few environments offer a seamless transition between different problem types. Codespec supports learners in practicing how to solve a programming problem as a Pseudocode Parsons problem, a Parsons problem, a Faded Parsons problem, a fix-code problem, or a write-code problem. The goal of the project is open-ended, we will work together to design a research project that compliments your interests and expertise.

Nature of Student Involvement: Students will code conceptualize new features, write code to develop features, or run a study to investigate something related to the platform.

Skills we are interested in (not all required):

• Experience or interest in computing education research, tools and environments.
• Experience with data science, learning analytics, student modeling, large language models.
• Preferred: CS (or other technical) major, HTML/CSS/JavaScript, Python, Django, VueJS, Figma skills.

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Project 6
Comprehensive Personalized Programming Practice Environment

Mentor: Paulo Carvalho, faculty

Description: Computer science education is increasingly critical for preparing well-trained professionals for the national economy and building a competitive workforce of the future. This project capitalizes on the power of generative AI and progress in learning science research to provide personalized learning experiences for computer science students. The system will recommend the most suitable learning activities for each student according to their current knowledge level and offer personalized feedback to support their progress. A sequence of experiments will lead to a better understanding of the kinds of practice opportunities (i.e., worked examples vs problems) and types of feedback messages that are most effective to each student depending on their prior knowledge and interest.

Nature of Student Involvement: Students will be involved in all steps of experimental research, data analytics, student modeling, and LLM-based material development.

Skills we are interested in (not all required):

• Quantitative data analyses
• Experience with experimental design and data collection
• Data science/learning analytics/student modeling experience preferred but not required
• Experience with LLMs preferred but not required

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Project 7 
Connecting Through Brick Clubs

Mentor: Jessica Hammer, Associate Professor of Learning Sciences Director, Center for Transformational Play

Description: We are launching Brick Clubs, which are facilitated LEGO play experiences, across the Pittsburgh region and beyond. We are using these clubs to study how shared play spaces can support cross-class friendships between participants. We have designed a set of interventions to supplement the standard Brick Club setup, and by summer we will have data collected from multiple field sites.

Nature of Student Involvement: The student will be participating in qualitative and quantitative data analysis. They will also participate in writing up our findings for multiple audiences, most likely an academic conference and a set of recommendations for Brick Club sites.

Skills we are interested in:

• Required: self-motivated, strong writing skills. 
• Preferred: prior experience with data analysis.

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Project 8
Designing Algorithms for Adaptive Extended Reality (XR)

Mentor: David Lindbauer, faculty

Description: Extended Reality (XR) interfaces allow users to interact with the digital world anywhere and anytime. By embedding interfaces directly into users' environments, XR interfaces can both enhance productivity and be less distracting than traditional computing devices such as smartphones.  In this research, we aim to design algorithms that create context-aware XR interfaces, i.e., interfaces that automatically adjust when, where, and how to display XR interfaces. These algorithms can be optimization-based or learning-based, and form the backbone of XR systems that continuously adapt to users' needs and requirements when they interact with XR systems in a wide range of applications, from productivity, entertainment, manufacturing, or healthcare.The research involves developing a novel algorithm for adaptive XR, and evaluating the approach in a comparative user study.

Nature of Student Involvement: Students will collaborate with other undergraduate and graduate students in creating the concept and planning of the research, and lead the implementation of the algorithm and the creation of the user study platform.

Skills we are interested in (not all required):

• Strong programming skill (c# or similar) 
• Experience with Unity or Unreal
• Interest in XR
   

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Project 9
Designing Context-aware AI-driven Assistants to Help Patients

Mentor: Mayank Goel, faculty

Description: We aim to guide patients through their recovery process using lightweight sensing and behavior modeling. The end-to-end solution will unify multimodal activity and behavior sensing on a watch with an AI-driven intervention agent. The system will use watch-based multimodal sensing to guide the patient. The agent will recognize the patient’s actions and behaviors and proactively intervene only as needed. It will augment doctors' understanding of patients' situations with a meaningful and appropriate level of explanation when a problem occurs.

Nature of Student Involvement: The student will work on development and evaluation of sensing and machine learning algorithms to support patients. They will work with the faculty members, clinicians, and graduate students as they contribute to the development of the end-to-end solution.

Skills we are interested in (not all required):

• Python programming
• Applied machine learning
• Optional: 
  • Mobile development
  • Designing for Human-AI interaction
  • Natural Language Processing

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Project 10
Enhancing Personalization and Accessibility in Virtual Reality Experiences

Mentor: Atieh Taheri, presidential postdoctoral fellow

Description: This research aims to explore how personalization and accessibility can improve user experiences in virtual reality (VR) environments. As VR becomes more prevalent, it is crucial to design interactions that cater to diverse user needs and preferences. The project will investigate various aspects of VR interaction, including movement, self-representation, and social presence, to develop inclusive experiences that empower users and promote positive engagement. By prioritizing inclusivity and adaptability, the project aims to accommodate users with varying abilities and preferences. Our findings will contribute to creating technologies that enhance user empowerment, foster identity expression, and build engaging virtual environments.

Nature of Student Involvement: Students will participate in designing and conducting VR experiments, assist in data collection and analysis, and engage in brainstorming sessions to propose inclusive design solutions.

Skills we are interested in (not all required):

• VR development skills (e.g., experience with Unity or Unreal Engine)
• Familiarity with basic research methods (qualitative and/or quantitative)
• Creativity in brainstorming and problem-solving
• Familiarity or interest in accessibility and inclusive design

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Project 11
Finding Where all the Educational Games Have Gone

Mentor: Erik Harpstead, Senior Systems Scientist

Description: Educational games have been a common topic of study for decades, however, a persistent challenge with the field has been supporting academic games to continue reaching audiences and serve as research instruments beyond the scope of initial efficacy studies or deployments. We are part of a long term project looking to build a better research infrastructure for using games as research instruments. To support this effort we need to better understand the challenges that have been faced by academic games in the past, with a particular focus on those that have not survived to the present day. This project will involve taking a census of the landscape of educational games from the past 10 years or more, finding how many of them are still discoverable, and reaching out to developers to identify challenges they encountered with long term support and research.

Nature of Student Involvement: The overall project will have three phases, which the REU will be involved in based on the pace of the work during the summer. First, we perform a targeted literature review from relevant venues to compile a list of games and evaluate their current discoverability. Next, we will circulate a survey to the authors of identified papers. Finally, we will follow up the survey with interviews of interested developers.

Skills we are interested in (not all required):

• Familiarity with literature review techniques
• Qualitative data collection techniques
• An interesting in game design would be helpful but not strictly necessary

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Project 12
Fostering Empathy Through Gaming While Ridesharing

Mentor: Jane Hsieh, graduate student advised by faculty Haiyi Zhu

Description: Ridesharing platforms like Uber and Lyft create and exploit information asymmetries to control drivers, pay them low and often unfair wages, and generally subject them to poor working conditions. These include hidden forms of labor, algorithmic discrimination, social isolation, job precarity, and a lack of standard employee benefits—all issues that many riders remain unaware of. While several attempts from the research and organizing communities have probed at auditing driver pay through data donations from drivers, the potential of leveraging contributions from riders are underexplored. Meanwhile, recent scholarship points to the promise of game play for developing user empathy. In this project, we are recruiting a programmer to help in developing a game that helps build empathy between riders and drivers while simultaneously allowing for data contributions from engaged passengers.

Nature of Student Involvement: Prototyping, pilot testing, deployment, and evaluation.

Skills we are interested in (not all required):

• Programming experience (preferably also in game development)
• Exposure to UX design and research
• Experience running user studies or experiments

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Project 13
Human-Centered Data Science and Visualization

Mentor: Adam Perer, faculty

Description: The Data Interaction Group (DIG) (https://dig.cmu.edu) has a mission to empower everyone to analyze and communicate data with interactive systems. Our group conducts research in computer science at the intersection of human-computer interaction, machine learning, data science, programming languages, and data management. This summer, we plan to build new tools for data scientists to help them better understand their data, which will hopefully result in better downstream machine-learning models derived from the data.

Nature of Student Involvement: Research, coding, and user studies.

Skills we are interested in (not all required):

• Programming experience
• Interest in data science and machine learning
• Web development skills

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Project 14
Intelligent Agents for Security

Mentor: Kyzyl Monteiro, graduate student advised by faculty Sauvik Das

Description: This project explores the design and development of intelligent agents to address persistent challenges in security and privacy for end-users. Privacy is often a secondary concern, considered only when breaches or issues arise, leaving users vulnerable to increasingly sophisticated attacks. Additionally, traditional security interfaces fail to accommodate the diverse abstraction levels required by different users, presenting barriers to usability and control - leading to under-sharing or over-sharing. With advancements in AI, including Large Language Models (LLMs) and Computer Vision (CV), there is an opportunity to create intelligent agents that simplify privacy and security, empower end-users, and proactively mitigate risks. Building on the lab's previous research—such as Spidey Sense, Bit Whisperer, and Smart Webcam Cover—this project continues the exploration of tangible and intelligent solutions to enhance user awareness, motivation, and ability to engage in privacy and security practices.

Nature of Student Involvement: Students will work on the ideation, prototyping, and testing of intelligent agents to enhance user privacy and security. This may include: Designing user-friendly interfaces and interactions powered by off-the-shelf LLMs and CV models. Developing and implementing functional prototypes that demonstrate the capabilities of intelligent agents. Conducting interviews and usability testing to refine designs and assess the impact of the prototypes.

Skills we are interested in (not all required):

• Programming and development skills (e.g., Python, frameworks like PyTorch or TensorFlow)
• Experience with integrating and fine-tuning LLM and CV models into interactive systems
• Familiarity with hardware prototyping and tangible computing concepts
• Ability to conduct interviews and analyze qualitative data for design improvements

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Project 15
Interview Study with Industry Privacy Practitioners Who Engage in Responsible AI Work

Mentors: 
Hank Lee, graduate student advised by faculty Sauvik Das and Jodi Forlizzi 
Wesley Deng, graduate student advised by faculty Motahhare Eslami and Ken Holstein

Description: Interview Study with Industry Privacy Practitioners Who Engage in Responsible AI Work

Nature of Student Involvement:

• Literature review (weeks 1–2): students will first conduct a focused literature review on responsible AI, and privacy and security, guided by the two PhD PIs.
• Design and pilot test interview study protocol (weeks 3-4): Drawing from the literature, students will draft the interview study protocol, including research questions and objectives, and an interview protocol. Students will conduct a pilot study with a small group of participants to test and improve the protocol.
• Conduct interview studies (weeks 5-7): Using the refined protocol, students will recruit and interview industry practitioners. They will handle participant outreach (with assistance from PhD PIs), scheduling, and conducting interviews.
• Qualitative analysis (weeks 8-9):  Students will perform qualitative analysis on the collected data via open coding techniques to identify key themes and insights.
• Final report and presentation (week 10) Students will synthesize their findings into a comprehensive report that includes:
  • A summary of the literature review.
  • Details of the study design and methodology.
  • Key insights from qualitative analysis.

Skills we are interested in (not all required):

• Ability to conduct literature reviews and identify research gaps
• Experience in designing and conducting user study
• Enthusiasm for topics related to ethical technology development, responsible AI, and privacy and security challenges in AI
• Good time and project management skills
   

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Project 16 
Mental Health Mobile Game    

Mentor: Jessica Hammer, Associate Professor of Learning Sciences Director, Center for Transformational Play

Description: We are creating a mobile game for teens that incorporates an existing, research-backed program for improving mental health. We expect our prototype will be deployed next year for in-school field studies, which will involve an impact evaluation. We are also working on a) inclusive design processes, to make sure our game helps the teens who need it most, b) techniques for fostering replayability, and c) the design of supportive in-game characters for behavior change.

Nature of Student Involvement: The student will be planning playtests, using qualitative and/or quantitative data, and then traveling to field sites to collect data from playtesters. The student will have the opportunity to participate in data analysis and results writing if desired.

Skills we are interested in (not all required):

• Required: organized, strong communication skills. 
• Bonus: prior experience with research design or data collection.

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Project 17
Power and Battery Free Interactive Objects Made from Paper

Mentor: Scott Hudson, faculty

Description: This work will look at techniques for sensing user manipulation of paper objects (think pop-up books and related) with only very minimal extra conductors added (e.g., copper foil conductors).  This sensing will be done with "back-scatter" radio frequency techniques which are able to detect the manipulation of the objects by the user on the basis of the radio frequencies they reflect or absorb in various configurations.  

Nature of Student Involvement: Work will include components of both technical design for the back-scatter sensing using RF techniques, and physical design of the paper objects through paper design (likely addressed by different team members).

Skills we are interested in (not all required):

• Antenna design or other RF design experience would be perfect, but not fully expected; other more general EE experience is helpful.
• Experience with physical design e.g., with paper.

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Project 18
Restoring Civic Participation Through AI-enabled Reporting

Mentor: Jeff Bigham, faculty

Description:  The decline of local news outlets has disrupted the vital communication between elected officials and their constituents, leading to decreased awareness, misrepresentation of public preferences, and diminished accountability. Treating effective governance as a service design problem, this project seeks to restore this critical service by reimagining the role of journalists—not just as informers but as representatives of the community. Building upon a formative study involving key stakeholders—including elected officials, constituents, local media, and civic organizations—we aim to prototype AI-enabled tools that make the reporting process more efficient and representative. These tools will empower journalists to better understand and convey the needs and preferences of the community, thereby enhancing the quality and reach of governance.  

Project Goals:

• Design and Develop AI Tools: Create prototypes that assist journalists in data collection, analysis, and reporting to more accurately represent constituent interests.
• Enhance Communication Channels: Improve the flow of information between residents and elected officials by exploring the relative levels of 
  engagement in different news formats.
• Evaluate Effectiveness: Assess how AI-enabled tools impact the efficiency of reporting and contribute to restoring effective governance.

Nature of Student Involvement: 

• Prototype Development: Design and develop AI prototypes using machine  learning techniques such as natural language processing, sentiment analysis, and data mining. 
• Testing and Iteration: Implement prototypes and conduct user testing to gather feedback, refining the tools based on findings. 
• User Research: Collaborate with the research team to conduct interviews and gather insights from journalists, constituents, and officials to identify needs and challenges. 
• Data Analysis: Analyze user interactions and data to evaluate the effectiveness of the prototypes. 
• Team Collaboration: Participate in regular meetings, contribute ideas, and work closely with a multidisciplinary team.

Skills we are interested in (not all required):

• Programming Skills: Proficiency in Python or similar programming languages; experience with AI/ML frameworks like TensorFlow or PyTorch.
• Machine Learning Knowledge: Understanding of machine learning concepts, natural language processing, and data analysis techniques.
• Design Experience: Familiarity with user-centered design principles and prototyping tools.
• Interest Areas: A passion for journalism, civic engagement, public policy, or service design is a plus but not required.

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Project 19
Social Cybersecurity: Applying Social Psychology to Improve Cybersecurity

Mentor: Isadora Krsek, graduate student advised by faculty Laura Dabbish and Sauvik Das

Description: Past research investigating the human side of cybersecurity has treated people as isolated individuals rather than as social actors acting within a web of relationships and social influence. The goal of this project is to develop and evaluate novel techniques leveraging insights from psychology on social influences to improve people’s awareness of cybersecurity, as well as empower them with the knowledge and motivation to act securely. We currently have two avenues in this domain that we are recruiting for: The first leverages theories from social psychology (social influence) and game design (embedded design) to improve users' cybersecurity behavior and enhance the adoption of expert-recommended tools and best-practices. To this end we are building Adulting101: an iOS-based mobile app with covert intentions to improve young adults’ adoption of cybersecurity tools & best-practices, through framing these responsibilities through the lens of adulting. The second avenue explores the development and evaluation of privacy-preserving AI tools – specifically within the context of online support seeking (e.g. asking for advice on leaving abusive relationships, or finding trans-friendly healthcare providers) - defining dilemma of modern social media is helping users balance the social benefits they reap through online self-disclosure versus the privacy risks associated with those disclosures. To this end we are exploring the development and evaluation of a tool with the intentions to empower users seeking online support (e.g. through Reddit) through improving their awareness of self-disclosures online that may pose a threat to their privacy.

Nature of Student Involvement: Students on either avenue of this project will aid in the backend and/or front-end development of one of the tools described in the project summary. The student(s) will work to implement UI/UX  of the selected tool based on existing wireframes, and help bring this tool to a minimum viable product state for research. If time allows, the student will also have the opportunity to assist in conducting a field study evaluation of the tool.

Skills we are interested in (not all required):

• Experience with mobile app development (language: Swift) 
• Experience with front/back-end browser extension development (languages: HTML, CSS, JavaScript, Python)
• Experience in quantitative data analysis (e.g. R, Python, etc.)

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Project 20
Supporting Middle School Math Homework with AI through Goal Setting and Experiments

Mentor: Conrad Borchers, graduate student advised by faculty Vincent Aleven and Ken Koedinger

Description: This project aims to enhance AI-driven tools that provide personalized support for middle school mathematics homework, benefiting students and parents. The project is situated in a series of classroom experiments, where we evaluate the effectiveness of different homework interventions concerning student learning gains and learning rates modeled on the level of problem-solving step attempts in tutoring systems. In addition to ongoing analyses of experimental data from these classroom studies, we have designed and developed different intelligent systems that help students and parents manage homework efforts and support. As context for this summer’s project effort, we have piloted a goal-setting system that helps students manage their homework efforts over time and receive data-driven feedback on their achievement, but we plan to improve it using theories of self-regulated learning and insights from data analysis of student interactions. Additionally, we are refining a chat recommendation module for parents that aids hand-on homework support, leveraging intelligent tutoring system recommendations and language models.

Nature of Student Involvement: Two students will contribute to this project: one will focus on researching and developing our homework support tools (e.g., goal setting), and the other will focus on data analysis from classroom intervention studies using our tutoring systems. The engineering student will focus on researching and developing improved goal-setting tools and may also assist with integrating the chat module. The data analysis student will work with experimental data, including system interaction logs, using R and Python for analysis.

Skills we are interested in (not all required):

• Engineering Student Requirements/Preferred Skills:
  • Proficiency in web development, ideally with Vue.js or a similar framework.
  • Experience with frontend and backend integration.
• Data Analysis Student Requirements/Preferred Skills:
  • Proficiency in R or Python for data analysis.
  • Experience with statistical analysis of experimental data is a plus.
  • Knowledge of machine learning, cognitive modeling, and working with system interaction log data is a plus.

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Project 21
tronicsBot: An AI Partner for Creative Electronics Design and Development

Mentor: Nik Martelaro, faculty

Description: This project will develop an AI assistant to help people develop creative electronics. The AI assistant will help with tasks such as component selection, project planning, code generation, and circuit and PCB design. We will explore what widely-available LLMs and VLMs can do in this space and document the capabilities. We will also develop conversational strategies for how an AI should work with a designer. The main project deliverable will be a documentation of experiments trying to use an LLM to support creative electronics work.

Nature of Student Involvement: The student will experiment with LLMs to see what they can help with along the design and development of creative electronics projects. The student will take detailed documentation of their experiments. The student will build physical and functional prototypes of numerous creative electronics projects to show how the tools can support real-world development of hardware devices. The student will reflect on their experience and write memos on where the AI works, where it does not, and how we might develop new tools to support creative technologists.

Skills we are interested in (not all required):

• Skills with creative electronics like Arduino, Raspberry Pi, sensor and actuator breakout boards
• Good documentation skills including written documentation, photos, and videos
• Good organization skills for planning tasks and communicating project timelines
   

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Project 22
Visualizing AI for Treatment Decisions in Healthcare

Mentor: Adam Perer, faculty

Description: We are working on building interactive visualization tools to help physicians and other healthcare providers diagnose and treat diseases more effectively using artificial intelligence (AI) algorithms. These tools leverage historical patient trajectories to identify and recommend optimal treatment plans to clinicians in real-time. However, providing these recommendations to clinicians is no simple task - clinicians often dismiss them as untrustworthy, or pick and choose aspects of the recommendation to follow. We are looking for a motivated student to work with us and our clinical collaborators to understand how best to deliver AI-generated recommendations.

Nature of Student Involvement: Students will interact with healthcare experts and design new interactive, visual interfaces.

Skills we are interested in (not all required): Interest in healthcare, data visualization, and web-based programming.
 

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Project 23
VTutor

Mentor: Eason Chen, graduate student advised by faculty Ken Koedinger

Description: VTutor is an open-source SDK (Software Development Kit) that allows developers to integrate animated pedagogical agents into front-end Web Applications easily with few lines of code and iframe. For the prototype, please view it at https://vtutor.vercel.app

Nature of Student Involvement: We’re seeking a talented and motivated student to join the VTutor project! If you have experience with Unity and frontend development, this is a fantastic opportunity to contribute to an innovative educational technology initiative. You will assist in Unity and front-end SDK (Typescript) development to improve the VTutor SDK. Moreover, you will help implement some experiment platforms with our team to prepare VTutor for submission as a research paper at upcoming conferences like LAK or CHI 2026 (deadline in September).

Skills we are interested in (not all required):

• Proficiency in Unity.
• Proficiency in Typescript and any frontend framework (prefer React).
• Know how to write clean and maintainable code.
• Passion in VTutor. If you are a fan of any VTuber, that is a BIG PLUS.

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Project 24
Workplace Surveillance Tracker

Mentor: Cella Sum, graduate student advised by faculty Sarah Fox

Description: As work becomes more remote and distributed, a range of surveillance technologies are increasingly being used by businesses to track and monitor employees. While workers express concern about risks to their health, safety, and privacy, they also face a lack of transparency and autonomy around the use of these systems. This project aims to design and develop a public workplace surveillance tracker to create more transparency around this issue. We aim to populate this tracker using cases pulled from several data sources, including: news outlets, academic publications, legal cases, government agency reports, vendor websites, among others. Additionally, we will provide mechanisms for workers to submit their own cases with the ability to include personal testimonials of their experiences, while preserving their privacy. By offering detailed information from various credible sources and including firsthand accounts from workers, the tracker will serve as a resource to inform employees, researchers, and policymakers about current surveillance practices and trends in the workplace and help them to take action.

Nature of Student Involvement: Students will support the design, development, and testing of the workplace surveillance tracker, and will create a series of low and high-fidelity prototypes.

Skills we are interested in (not all required):

• An interest and curiosity around the intersection of technology, labor, and justice
• Web development (including advanced javascript)
• UI and UX Design
• Qualitative and quantitative data analysis

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Project 25 
Advancing Mental Health Training with Realistic Virtual Patients

Mentors:  graduate students Angela Chen and Jessie Mindel, advised by faculty Haiyi Zhu, Sherry Wu, and Robert Kraut 

Description: Are you passionate about technology and its impact on mental health? Our research team is pushing the boundaries of Virtual Patients (VPs)—conversational agents designed to simulate real-world patient interactions—to revolutionize mental health training.

Our research focuses on two approaches to simulated mental health training. First, while traditional VPs focus on single-session therapy interactions, we aim to develop multi-session VPs that replicate the full therapy journey, offering more realistic practice. Second, we aim to design and provide personalized, contextually relevant opportunities for trainees to learn how to communicate supportively, including adaptive tools for prompting and scaffolding reflective practice. By leveraging Large Language Models (LLMs), we’re building a scalable, cost-effective training framework that helps novice mental health providers understand long-term client progress, dynamic mental state shifts, and evolving therapy needs.

Nature of Student Involvement: The student will work closely with the project lead and will be involved in different aspects of the project including design, development and research.

Skills we are interested in (not all required):

• Programming experience 
• Interested in HCI research, including conducting user studies
• Some experience with LLM is preferred