Nicholas Gardella

Gardella’s teaching and research is primarily focused on introductory computing and cyber-human systems.

Nicholas Gardella

Nicholas Gardella

Assistant Professor of Computer Science

Program Affiliations

Education

  • Ph.D., Systems Engineering, University of Virginia (2026)
  • Graduate Certificate, Cyber-Physical Systems, University of Virginia (2026)
  • M.Eng., Systems Engineering, University of Virginia (2023)
  • B.S., Computer Science, Virginia Tech (2021)

Research

Research interests include computing education, human-AI interaction, human-computer interaction, cyber-physical systems, and web development.

Current Research

As Artificial Intelligence (AI) enters computing and education, Dr. Gardella is interested in how students, educators, and software professionals can best use it to promote human flourishing. He conducts empirical studies with human participants to understand and improve human interactions with cutting-edge computing technology. His recent works have focused on the use of code-generating AI tools by novice programmers as compared to individual and human-human pair programming paradigms.

Teaching

Dr. Gardella teaches introductory computing, human-computer interaction, systems programming, web development, and cyber-physical programming. He embraces generative AI in the classroom but maintains traditional standards for rigorous coursework and compulsory social learning. He has technical expertise in data science (R and Python), software containerization, Bluetooth®︎ Low Energy, front-end web development, and applications of AI and Machine Learning (ML) models under resource constraints.

Selected Publications

  • “Audio PhD Dissertation of Nicholas Gardella | Responsible and Equitable Use of AI Code Generators in Computer Science Education.” [Online]. Available: https://doi.org/10.18130/V3/VM1IPO
  • N. Gardella, M. L. Bolton, and S. L. Riggs, “Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation.” [Online]. Available: https://arxiv.org/abs/2604.18948
  • N. Gardella, J. Prather, J. Leinonen, P. Denny, R. Pettit, and S. L. Riggs, “Fast and Forgettable: A Controlled Study of Novices’ Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms.” [Online]. Available: https://arxiv.org/abs/2604.18538
  • N. Gardella, R. Pettit, and S. L. Riggs, “Performance, Workload, Emotion, and Self-Efficacy of Novice Programmers Using AI Code Generation,” in Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, Milan Italy: ACM, Jul. 2024, pp. 290–296. doi: 10.1145/3649217.3653615.

  • N. Gardella, J. Shelton, I. Graßl, and S. Riggs, “HBCU Student Perspectives on Identity, Persistence, and Code-Generating AI in CS Education: A Case Study,” in Proceedings of the 25th Koli Calling International Conference on Computing Education Research, Koli Finland: ACM, Nov. 2025, pp. 1–12. doi: 10.1145/3769994.3770015.

  • N. Gardella and S. L. Riggs, “Establishing natural tactile mappings: Mapping tactile parameters to continuous data concepts,” IEEE Transactions on Haptics, 2024, Accessed: Mar. 29, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10417733