My goal as an advisor is to help students become independent, thoughtful, and socially responsible researchers and software professionals. I view mentoring as a collaborative process grounded in clear communication, mutual respect, intellectual curiosity, and continuous growth.
Students are not simply assigned tasks within my projects. I encourage them to participate in shaping research questions, making methodological decisions, critically examining evidence, and communicating the significance of their work. As students gain experience, they receive increasing independence and responsibility.
I welcome inquiries from motivated undergraduate, master’s, and doctoral students interested in conducting research at the intersection of software engineering, human-computer interaction, open-source software, and responsible artificial intelligence.
My primary research areas include:
Human aspects of software engineering: Understanding the experiences, behaviors, challenges, and needs of software developers, students, educators, and technical communities.
Human–AI collaboration in software engineering: Investigating how people use AI assistants and agents for programming, reviewing code, learning, problem-solving, and other software engineering activities.
Software engineering education: Designing and evaluating educational practices that prepare students to use AI critically, responsibly, and effectively.
Responsible and evidence-based AI use: Examining overreliance, verification practices, transparency, academic integrity, and the appropriate use of large language models.
Open-source software: Understanding barriers to participation and designing interventions that help students and newcomers contribute successfully to open-source communities.
Sustainable AI interaction: Investigating how interface design, user behavior, and organizational practices can reduce the environmental and computational costs of AI-assisted work.
Developer well-being and technostress: Examining emotional exhaustion, anxiety, cognitive demands, and other well-being concerns associated with emerging software technologies.
Community-engaged computing: Collaborating with students and community partners to develop socially meaningful technologies for Hawai‘i and other communities.
Research projects may involve surveys, interviews, controlled experiments, field studies, qualitative and quantitative analysis, literature reviews, software development, interface design, and evaluations with students, developers, educators, or community partners.
Students from computer science, software engineering, computer engineering, information science, human-computer interaction, education, psychology, communication, data science, and related fields are encouraged to contact me.
Strong candidates normally demonstrate:
Curiosity about research and willingness to learn.
Interest in human-centered software engineering or responsible AI.
Effective written and verbal communication.
Reliability, organization, and the ability to meet agreed-upon deadlines.
Openness to constructive feedback and collaborative work.
Careful attention to research ethics and data quality.
Programming experience is valuable for projects involving tool development, but it is not required for every project. Some studies primarily involve research design, literature analysis, interviews, surveys, qualitative coding, statistical analysis, educational interventions, or community engagement.
Previous research experience and publications are welcome but are not required. More important qualities include motivation, intellectual curiosity, commitment, and a clear connection between your interests and my research.
Send an email to isantos3@hawaii.edu with the subject line:
Prospective Student – [Degree Level] – [Your Name]
For example:
Prospective Student – MS – Jane Doe
Please include:
A brief introduction describing your current institution, academic program, and degree level.
Your research interests and how they connect with one or more of my projects or publications.
Why you would like to work with me specifically.
Relevant experience, including research, coursework, employment, community engagement, programming, data analysis, or writing.
Your preferred starting semester and expected graduation timeline.
Your CV or résumé.
An unofficial transcript.
Optional supporting materials, such as a paper, thesis, technical report, portfolio, GitHub repository, writing sample, or project description.
You do not need to submit a fully developed research proposal in your first message. However, you should identify one or two research topics that genuinely interest you and explain why.
Please review my research and recent publications before contacting me. Your message should demonstrate a specific connection between your interests and my work.
Generic messages sent to many faculty members are difficult to evaluate and may not receive a response. Avoid simply stating that you are interested in “AI,” “machine learning,” or “software engineering.” Instead, explain the particular problem you hope to study.
Using generative AI to improve grammar or organization is acceptable. However, your message should accurately represent your experiences, interests, and writing. Do not include fabricated publications, skills, projects, or claims of having read papers you have not examined.
Admission decisions are made through the university’s formal processes. An encouraging response from me should not be interpreted as an offer of admission, employment, funding, or an assistantship.
Consider whether you can answer the following questions:
Which of my research areas interests you most?
What research problem would you like to understand or address?
Which of my publications or projects is most closely related to your interests?
What skills or perspectives could you bring to the research group?
What new skills would you like to develop?
How much time can you consistently dedicate to research?
What academic or professional goal would this experience help you achieve?
Thoughtful answers to these questions will help determine whether our research interests and mentoring expectations are a good match.