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All interested faculty are welcome to join our team and we encourage you to contact either Angela Bielefeldt or Alessandro Roncone for initial information, orientation and subscription to our mailing list. 

In 2023, the EE-AIL IRT Seed grant program will award grants ranging from $4,000 to $50,000 to teams of faculty for a total of about $120,000. In particular, for year three we will expand our offering to support three distinct activities:

• Research Grants ($20,000 to $50,000): The IRT is interested and invested in funding new, early-stage, interdisciplinary collaborations within the College of Engineering and Applied Science (CEAS) and beyond. Seed grants can include purposes such as funding for pilot studies, student support, research tool development, or collaboration-building activities that may lead to future proposals. Particular focus will be given to proposals focused on supporting graduate students or postdocs for an entire semester (or close to it). Faculty salary is allowed but not recommended.
• Proposal Writing Grants (up to four weeks of summer salary): Complementary to research-focused grants, we will facilitate and support grant writing efforts from faculty, in the form of faculty summer salary that must be devoted to proposal writing. These four weeks can be spread among multiple faculty. Importantly, we acknowledge that writing interdisciplinary proposals is particularly challenging and we would be happy to work with you to draft a strong and convincing plan of work to succeed in your grant writing activity.
• Travel Grants (up to $4,000): We encourage our IRT faculty to participate in and attend conferences that are relevant to the mission of the IRT. We hope this activity will facilitate future spillover of research ideas from AI to education and vice-versa. To this end, we will support travel for faculty to attend conferences “on the other side” – AI researchers attending education conferences, and vice-versa.

In all these efforts, the lead should be a member of the College of Engineering and Applied Science (CEAS), but additional collaborators may be inside or beyond CEAS (including outside CU at K12 schools or in industry). Faculty are encouraged to take a broad and creative perspective on how these funds can support the goals of the IRT.  The purpose of seed grant proposals is not just limited to these activities, grants can also include non-traditional activities such as web page content development, acquisition of professional illustrations for upcoming proposals, video production, outreach material, etc. 

Investment goals 

• Catalyze new research collaborations at the intersection of EER and AI
• Enhance the reputation of CU in Engineering Education and Artificial Intelligence research
• Grow infrastructure and research capacity (people, expertise, equipment)
• Compete for ‘center-scale’ and other large research grants

Phase two seed grant funded projects (2021-2022)

• "Developing AI agents to Support Teaching Staff in Large Programming Classes" – Ashutosh Trivedi (CS), Gowtham Kaki (CS), Fabio Somenzi (ECEE)
• "Proposal Writing Grant for NSF Research Coordination Network (NSF 17-59a4)" – Tom Yeh (CS)
• “Design of an AI-Augmented Learning Thread for Environmental Engineering Curriculum” – Azadeh Bolhari (CEAE – EVEN), Gunther Thiele (Freie Universitat Berlin)
• “Target-word Selection and Story Generation for Vocabulary Growth Support for Bilingual Preschoolers” – Katharina Kann (CS) and Eliana Colunga (Cognitive Psychology and Neuroscience)
• “Automatic Processing of Free-text Student Responses to In-class Surveys” – Katherine Goodman (ATLAS), Jean Hertzberg (MCEN), Katharina Kann (CS)

Phase one seed grant funded projects (2020-2021)

• "Human-AI Collaborative Programming: Integrating Reinforcement Learning and Formal Requirements in Programming Curriculum" – Ashutosh Trivedi (CS) and Fabio Somenzi (ECEE)
• "Supporting Language Learning in Bilingual Preschoolers" – Katharina Kann (CS) and Eliana Colunga (Cognitive Psychology and Neuroscience)
• "Teaching machine learning to non-CS undergraduate students: An application of ML to predict concrete mechanics" –  Mija Hubler (CEAE) and Geena Kim (CS)
• "Students Epistemological Beliefs 91��Ƭ�� Science: Comparison of Biology and CS" – David Quigley (ICS / CS) and Melanie Peffer (ICS)
• "Can we use smartphones and machine learning to learn about pavement deterioration?" – Cristina Torres-Machi (CEAE), Qin Lv (CS), Angela Bielefeldt (CEAE, E+)
• "Towards equitable robot tutoring: intersectional analysis of human-robot interaction in racially diverse classrooms" – Tiera Chantè Tanksley (EDUC) & Alessandro Roncone (CS)
• "Aesthetics for Affect in Engineering Education" – Jean Hertzberg (MCEN) and Katherine Goodman (CU Denver)
• "An Intersectional Analysis of student experiences and perceptions in First-Year engineering projects classes" – Mindy Zarske (E+), Mike Soltys (E+), Angela Bielefeldt (CEAE, E+)