Dr. Jennifer Kim Jin is an Associate Professor in the School of Computer Science & Engineering at California State University, San Bernardino. Her research focuses on artificial intelligence, machine learning, and data science, with applications in education, healthcare, and interdisciplinary domains. She has published in venues such as Applied Sciences, IEEE CAI, IEEE EDUCON, and IJMS, and has collaborated with Loma Linda University Medical Center on AI for hepatocellular carcinoma detection and diagnosis.
Her current work explores the role of generative AI and large language models (LLMs) in education, examining their effectiveness as pedagogical tools and their broader ethical implications. She also conducts applied machine learning research in medical imaging and public health.
Dr. Jin has mentored over 75 student research projects, many leading to publications and awards at institutional and statewide competitions. She co-developed the M.S. in Applied Data Science program and integrates project-based learning, near-peer mentorship, and AI-enhanced pedagogy into core computing courses, advancing equitable, workforce-ready opportunities in STEM education.
Dr. Jennifer Kim Jin is an Associate Professor in the School of Computer Science & Engineering at California State University, San Bernardino. Her research focuses on artificial intelligence, machine learning, and data science, with applications in education, healthcare, and interdisciplinary domains. She has published in venues such as Applied Sciences, IEEE CAI, IEEE EDUCON, and IJMS, and has collaborated with Loma Linda University Medical Center on AI for hepatocellular carcinoma detection and diagnosis.
Her current work explores the role of generative AI and large language models (LLMs) in education, examining their effectiveness as pedagogical tools and their broader ethical implications. She also conducts applied machine learning research in medical imaging and public health.
Dr. Jin has mentored over 75 student research projects, many leading to publications and awards at institutional and statewide competitions. She co-developed the M.S. in Applied Data Science program and integrates project-based learning, near-peer mentorship, and AI-enhanced pedagogy into core computing courses, advancing equitable, workforce-ready opportunities in STEM education.