In the following, an analysis of an e-learning technology through the lens of didactic/mimetic, authentic/synthetic, and transformative/reflexive pedagogies is discussed.

An excellent example of an e-learning technology that dynamically integrates various pedagogical approaches is an Interactive Online Coding Platform with Integrated Generative Artificial Intelligence (GenAI) Feedback (e.g., platforms like LeetCode, HackerRank, or specialized secure coding environments with AI tutors). In its initial stages, such platforms often lean on didactic and mimetic pedagogy. They deliver structured lessons on programming fundamentals, algorithms, or secure coding principles through concise text, video lectures, and explicit code examples. Students engage in mimetic learning by observing expert-provided solutions, tracing code execution, and then replicating patterns in templated exercises or debugging given code snippets. The GenAI tools' feedback here primarily serves a corrective didactic purpose, guiding students towards the "correct" solution by highlighting syntax errors or logical flaws, thereby reinforcing foundational knowledge through iterative imitation.

Moving beyond foundational knowledge, these platforms transition into fostering authentic and synthetic pedagogy. Students are presented with increasingly complex, real-world coding challenges or simulated project environments that mirror actual industry problems, such as building a secure web API or optimizing a database query. This requires a synthetic approach, as learners must combine knowledge acquired from various modules—like data structures, network protocols, and security best practices—to architect a comprehensive solution. The GenAI tools' feedback evolves from simple correction to providing suggestions for optimization, alternative approaches, or security vulnerabilities, thereby pushing students to engage in higher-order thinking and problem-solving, much like a real-world software development scenario.
Finally, the most advanced application of such a platform can embody transformative and reflexive pedagogy. Through continuous AI-driven performance analytics, personalized learning paths are generated, guiding students to address conceptual gaps or challenging them with problems that push their understanding beyond current limits. The GenAI tools' feedback, rather than just pointing out errors, can prompt students to reflect on their problem-solving strategies, articulate their design choices, and critically evaluate the trade-offs in their solutions. This reflexive process, coupled with the iterative nature of coding, debugging, and receiving intelligent feedback, encourages students to not only master skills but also to re-evaluate their own cognitive processes and approaches to learning, leading to a truly transformative shift in their understanding and capabilities as software developers and cybersecurity professionals.

References:

1- Xu, J. ed., 2024. Teaching and Learning in the Digital Era: Issues and Studies. World Scientific.

2- Frankford, Eduard, Clemens Sauerwein, Patrick Bassner, Stephan Krusche, and Ruth Breu. "Ai-tutoring in software engineering education." In Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training, pp. 309-319. 2024.