Application Documents: In addition to your resume, you may optionally submit a portfolio with your application.
Only undergraduate students seeking their first co-op are eligible for this role – do not apply if you have previously completed 1+ co-ops.
Project Description:
As AI technology becomes increasingly integrated into various aspects of our lives and professions, it’s imperative to adopt a human-centric approach when designing sociotechnical AI systems. This approach ensures that we safeguard stakeholders from potential harm while harnessing their diverse experiences and perspectives to inform the design process. The primary goal of this project is to study some of these differences, such as prior technical and domain familiarity, cognitive disparities, biases, and attitudes and behaviors toward AI to develop innovative methods for mitigating or incorporating these differences into the design of AI systems. This focus extends to critical and emerging systems such as large language models (LLMs), chatbots, AI-fueled decision-support systems, and visual analytics tools. To this end, we are looking to recruit an undergraduate co-op candidate to expand our ongoing research and development of human-AI collaborative systems. Working together with the candidate, we will formalize and define the project scope and research questions. The primary responsibility of the co-op candidate will be to lead the development and design of a functional prototype for an AI system, with a specific focus on interface, visualization, and interaction design. This prototype will serve as a platform for the research team’s hypothesis testing. The co-op will have a chance to participate in human-centered experiment design and help conduct user studies. The co-op may also acquire experience in working in interdisciplinary teams, particularly between ML/AI engineers and HCI researchers, and work on oral and written presentation skills to communicate their ideas to and beyond the team, especially to publish the work to the research community.
Descriptions of Student Tasks and Responsibilities:
Qualifications:
– Undergraduate student from fields of Computer Science (CS), human-centered computing, or related fields (required)
– Proficiency in full-stack web-based programming, with a minimum prerequisite of having completed relevant CS courses (required)
– Demonstration of strong web-programming skills to lead the development and design of intelligent user interfaces. Example skills include, but are not limited to: JavaScript, Node.js, React.js / Vue.js, MUI, Bootstrap.js, and D3.js (required)
– Familiarity with human-centered research, such as qualitative or quantitative experiment design, e.g., having taken HCI, UXD, or similar courses (preferred)
– Familiarity with quick scripting languages, preferably in Python or R, for data analysis (Preferred)
– Familiarity with machine learning and AI concepts; technical familiarity is not required but can be beneficial (e.g., being able to implement or tune a model)
Responsibilities:
– Rapid, user-centered prototyping and design sketches
– Implementing web-based intelligent user interfaces with back-end and database integration
– Participating in data analysis tasks; engaging in discussions on the analysis and formalizing the findings
– Regularly providing project updates throughout the summer and presenting the final project outcomes (oral and written) at the end of the internship.
– Leading the project and documenting discussions, development, and decisions for transferable, reproducible research, to ensure the possibility for future continuation of the project.
– Take part in formalizing hypotheses, as well as designing, implementing, and conducting user-centered experiments
What specific skills and knowledge do you think the students will gain from working on the research project?
Depending on the specifics of the project and the prior qualifications of the intern, they may gain some or all these skills below:
– Gain experience and learn about conducting user-centered research, particularly in the fields of human-centered AI, ethical and responsible AI, explainable AI, and visual analytics;
– Learn about other aspects of interdisciplinary problem-solving for sociotechnical AI systems, while working on real-world problems and domains
– Learn to consider and consistently integrate ethical experiment practices into every stage of HCI research;
– Gain proficiency in statistical and qualitative design and analysis, utilizing tools like R, Python, and research software such as ATLAS.ti, for effective data interpretation and scientific presentation.
– Gain experience in conducting literature reviews for unfamiliar fields or topics, while utilizing literature review management software like Zotero or Mendeley;
– Acquire experience in working in interdisciplinary teams, particularly between ML/AI engineers and HCI researchers;
– Incorporate/improve web-based programming experience while learning visualization frameworks and libraries like D3.js and Observable;
