AI in Teaching & Learning at MIER College of Education (Autonomous): Baseline Research Report -2025

Abstract

This baseline study examines the readiness, perceptions, and practices of teachers and students regarding the use of Artificial Intelligence (AI) in teaching, learning, and research at MIER College of Education. The survey involved 32 teachers and 358 students, offering a comprehensive overview of AI integration within the institution. The findings will inform targeted interventions under the Commonwealth of Learning (COL) project on AI policy development and capacity building. Among teachers, AI usage is widespread, with over 80% reporting regular use of AI for academic tasks such as lesson preparation, content creation, assessment design, and research writing. A significant majority recognised strong pedagogical gains: 78% perceived improved student engagement, 72% reported enhanced feedback processes, and over 70% noted a reduction in academic workload due to AI support. At the same time, teachers identified key challenges—68% expressed concerns about data privacy, 61% highlighted the absence of clear policy guidelines, 57% stressed risks to academic integrity, and many noted limitations in institutional infrastructure and training. Teachers strongly recommended that AI governance be coordinated by units such as the Centre for Educational Technology (CET), the ICT Unit, and the Academic Office. Student findings reveal high levels of AI adoption: over 90% of students utilise AI tools, with ChatGPT, Gemini, Bing AI, Grammarly, and QuillBot being the most popular. Students primarily use AI for concept clarification (73%), assignment preparation (69%), summarising readings (65%), and enhancing writing quality (62%). Nearly 68% agree that AI saves time, and 63% believe it offers effective learning support beyond classroom hours. Despite this, students expressed concerns about inaccurate outputs, over-reliance, reduced originality, and ethical risks. Many requested more training workshops and institutional access to premium AI tools to address the limitations of free platforms. The sample profile shows a mainly young and digitally-focused student group, with 77.7% females, 85% aged between 18 and 25, and strong representation from Psychology, Political Science, Special Education, and Education programmes. Teachers form a diverse academic staff across different specialisations with varied but increasing experience in AI use. Overall, the data show strong motivation and emerging skills among teachers and students, balanced by clear needs for structured AI literacy, institutional policies, infrastructure improvements, and capacity building. These insights form the basis for the upcoming COL-led workshops and institutional AI policy initiatives.