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.
Over the first year, the bulk of the IRT funding will be devoted to seed grants. These will be largely unconstrained to support research related to the IRT theme, with a range of funding from $5,000 to $20,000. The funding may be used for any justifiable expenses, which could include graduate student support or travel, for example. Each grant should include a significant collaboration between units and/or with an industry partner. The selection criteria includes fostering new research collaborations within CU, fostering new research collaboration with industry or K-12 schools, and demonstrated potential for external funding after the grant. Particular attention will be given to projects focused on preliminary research that may become instrumental to apply to large proposals that will help the IRT mission.
Projects with multiple researchers are recommended, and a Principle Investigator from within CEAS must be identified who will manage the award. A single person may not be a PI on more than one proposal. A single individual may receive funding from no more than two proposals.
- 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 About 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+)