User Engagement Analysis of TeacherMatic: A Study on the Application of AI Large Language Models in Teaching Practices within the UK Further Education Sector

This study focuses on the UK further education sector, taking the TeacherMatic platform (an AI-assisted teaching tool for educators) as the object, using Python and Google Colab to analyze the impact of AI large language models on teaching practices. The original author is SaadiaAdnan, the research source is GitHub (link: https://github.com/SaadiaAdnan/User-Engagement-Analysis-of-TeacherMatic-using-Python-Google-Colab-), and the publication date is June 2026. The core objective is to explore the interaction patterns between teachers and AI tools and their actual impacts.

The UK Further Education (FE) sector faces challenges such as diverse student groups, heavy administrative burdens, resource constraints, and rapidly changing curriculum requirements. AI large language models bring possibilities for education, including automated content generation, personalized learning support, instant feedback, and reducing administrative burdens, but the application effects in real teaching environments need systematic verification.

TeacherMatic is an AI-assisted platform designed specifically for educators. Its main functions include curriculum plan generation, assessment design, differentiated teaching materials, feedback generation, and administrative document processing. Technically, it is based on advanced large language models, uses prompt engineering to ensure content meets educational standards, and optimizes the interface to lower the threshold for use.

Data collection uses mixed methods: quantitative data (platform usage logs, function frequency, etc.), qualitative data (teacher interviews, questionnaires, classroom observations), and control analysis (efficiency comparison before and after use). Technical tools include Python (main language), Google Colab (cloud environment), Pandas (data processing), Matplotlib/Seaborn (visualization), and SciPy/StatsModels (statistical analysis).

Expected outcomes include quantifying the time saved by AI tools, evaluating the improvement in teaching quality, understanding teacher acceptance, and student feedback. Policy implications can provide references for FE institutions' AI procurement, teacher digital skills training, ethical guidelines for AI applications, and evaluation of educational technology investments.

The research value lies in verifying the effects of AI tools in real environments, focusing on the further education sector, and being practically oriented. Potential limitations include possible restrictions on sample scope, results being affected by platform functions, and the rapid evolution of AI technology leading to the need for updates to findings.

TeacherMatic represents a new direction in educational technology—as a 'teaching assistant' that enhances teachers' abilities rather than replacing them. Educators need to maintain critical thinking (to review AI content), continuously learn collaborative skills, focus on student needs as the core, and pay attention to ethical issues (such as data privacy).