Abstract:

The initiative’s emphasis on reflection and continuous improvement, equips students with the self-awareness and adaptability needed for long-term success in coding and technology. The challenge of engaging students in their learning journey is getting more urgent in this age of ubiquitous distraction. This initiative is based on adaptive learning and teaching, rather than assigning the same assignment to all students, it introduces the concept of dynamic assignment. Based on their skill level, students are provided with tailored coding examples and exercises: beginners focus on foundational concepts like syntax and simple algorithms, intermediates tackle modular programming and data structures, while advanced learners engage in complex problem-solving, optimization, and real-world projects.

This initiative introduces a structured framework for teaching coding, focusing on student-led self-evaluation and continuous skill reassessment. Students assess their proficiency based on four key criteria: knowledge of programming concepts, problem-solving and debugging abilities, familiarity with tools and frameworks, and project experience. Proficiency levels are clearly defined as basic, intermediate, or advanced, providing students with benchmarks to evaluate their progress. Monthly checkpoints include updated coding challenges and reflective questionnaires to help students reassess their skills. Additionally, students maintain personal growth logs to document milestones, challenges, and reflections, fostering a culture of continuous improvement. This approach empowers learners to take ownership of their educational journey, develop self-awareness, and adapt to evolving skill requirements, enhancing both engagement and long-term coding competency. Instructors review self-assessments and provide guidance through one-on-one or group feedback sessions by level. During feedback sessions, instructors pair students at different levels to foster collaboration and knowledge sharing.

This initiative transforms coding education into an active, reflective, and personalized journey, equipping students with the tools for lifelong learning in technology. Before any assignment, instructors collect data from students to self-evaluate their coding proficiency as basic, intermediate, or advanced based on their reflections on knowledge, skills, and experience. Different levels of coding exercises will be assigned based on the proficiency level. Students self-train themselves by creating similar exercises using ChatGPT, which will allow them to discuss the ChatGPT approach.

Using AI-powered educational platforms in coding classes can make the learning experience more interactive, personalized, and efficient. These platforms leverage algorithms analysis and data structures to adapt to individual learners’ needs, helping students master coding concepts at their own pace while enhancing engagement.
With the goal of the project to improve student retention and timely graduation rates, we will answer the research question throughout the project: “How GenAI tools facilitates adaptive learning through dynamic interactions, adapts to various proficiency levels, and enhances student engagement through gamified and inquiry-based approaches.”

Keywords:

GenAi, Education, Adaptive learning.