VLDB 2026 Research / reviewers in the wild / expert
Daniel Mejia 0002
dblp:183/3686-2
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0004-0937-1294ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 1 first-author · 3 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A Framework for Integrating Generative AI in CS Courses
Vianey Martinez, Melina Salazar-Perez, Daniel Mejia 0002 |
ITiCSE (2) | 3 |
| 2025 | Bridging Academia and Industry: Leveraging Generative AI in a Software Engineering Course for Practical Industry ExperiencesabstractThe rapid adoption of generative AI across the tech industry demands a corresponding evolution in educational practices. By proactively incorporating generative AI, educational institutions can ensure their programs remain relevant and continue to provide students with the skills necessary for career success. This work presents an intro Software Engineering course, Software Development Studio (SDS), designed and implemented by Google in collaboration with faculty, to ensure students acquire industry-relevant skills. The course focuses on integrating generative AI tools into software engineering practices, mirroring the evolving methodologies used by professionals in the field. The curriculum emphasizes practical, real-world projects, providing early undergraduate computer science students hands-on experience using generative AI tools. Data collected during the Spring 2024 semester from students and faculty reveals a positive experience and enhancement of software engineering learning through the integration of generative AI. Daniel Mejia 0002, Ernest D. V. Holmes, Jenn Marroquin, Jamie Gorson Benario |
ITiCSE (1) | 1 |
| 2025 | Unlocking Potential with Generative AI Instruction: Investigating Mid-level Software Development Student Perceptions, Behavior, and AdoptionabstractGenerative AI tools are rapidly evolving and impacting many domains, including programming. Computer Science (CS) instructors must address student access to these tools. While some advocate to ban the tools entirely, others suggest embracing them so that students develop the skills for utilizing the tools safely and responsibly. Studies indicate positive impacts, as well as cautions, on student outcomes when these tools are integrated into courses. We studied the impact of incorporating instruction on industry-standard generative AI tools into a mid-level software development course with students from 16 Minority Serving Institutions. 89% of student participants used generative AI tools prior to the course without any formal instruction. After formal instruction, students most frequently used generative AI tools for explaining concepts and learning new things. Students generally reported positive viewpoints on their ability to learn to program and learn problem-solving skills while using generative AI tools. Finally, we found that students: reported to understand their code when they work with generative AI tools, are critical about the outputs that generative AI tools provide, and check outputs of generative AI tools to ensure accuracy. Jamie Gorson Benario, Jenn Marroquin, Monica M. Chan, Ernest D. V. Holmes, Daniel Mejia 0002 |
SIGCSE (1) | 5 |