Building Towards GenAI in Education: STREET Group's Prior Related Work
Building Towards GenAI in Education: STREET Group's Prior Related Work
Lydia Scholle-Cotton
With over two decades of experience in higher education administration, I am now fully engaged in researching the transformative impact of generative AI (genAI) in educational contexts. My extensive background includes substantial work in analytics, software implementation, and academic integrity studies, from my Master’s research examining faculty perspectives on academic dishonesty. This foundation informs my current exploration of genAI’s role and ethical implications within education. I am an active member of several research groups, including the Research in Education and Emerging Developments in Artificial Intelligence (REEAI) and Student Teachers Researchers Engaging with Emerging Technologies (STREET) at Queen’s University, both of which investigate genAI's impact on teaching, assessment, and policy adoption. Beyond these groups, I am also engaged in independent projects that explore genAI as a collaborative research tool and analyze its broader influence on academic environments to support inclusive policies and effective practices in educational technology.
My published related work :
Scholle-Cotton, L., & Childs, R. (forthcoming). Faculty perspectives on academic dishonesty: Evolving beliefs and opportunities for learning at two Canadian research universities. In Ethics and Integrity in Education (Practice) (Springer).
Stoesz, B., & Scholle-Cotton, L. (submitted - under review). Governance, academic integrity culture, and knowledge access in higher education: Comparative analysis of human and genAI methods. In A Research Agenda for Artificial Intelligence and Academic Integrity (Springer).
Malek EL Kouzi
Throughout my academic journey, from my bachelor’s studies to my PhD, I have consistently integrated educational technology into my research, particularly focusing on applications that enhance classroom engagement. I am a member of the STREET research group at Queen’s University. My work has consistently focused on integrating advanced technologies to enhance learning experiences. I have developed several interactive study applications that utilize augmented reality, collaborating closely with teachers to align these tools with classroom needs. These applications incorporate AI elements to adapt content dynamically and engage students in immersive, interactive learning. My previous work, including applications for studying complex topics like the human skeletal system, plant and animal cells, and geometric shapes, demonstrates the potential of AI-infused educational tools. This research lays a strong foundation for projects focused on generative AI (GenAI) in education, aligning closely with STREET’s goals to drive innovation and improve accessibility in classroom learning through tailored technology.
My published related work :
"An Educational Augmented Reality Application for Elementary School Students Focusing on The Human Skeletal System," 2019 IEEE VR Fourth Workshop on K-12+ Embodied Learning through Virtual & Augmented Reality (KELVAR), Japan.
"ON THE DEVELOPMENT OF A 3D SOLID SHAPES AUGMENTED REALITY, AUGMO: A GAME-BASED LEARNING APPLICATION", 2019 EDULEARN19, Spain.
"Augmented Reality Plant & Animal Cells: Design and Evaluation of an Educational Augmented Reality Application". Journal of Virtual Worlds Research CPF: Assembled 2019.
"FLCARA: Frog Life Cycle Augmented Reality Game-Based Learning Application" . International Conference on Human-Computer Interaction 2021