Purpose and Research Focus
This dissertation explores how AI-powered teaching assistants can transform computer science education through the application of heutagogy — a learner-centered theory emphasizing self-determined learning.
The core research question is how virtual AI systems (like the proposed VAITA) can empower students to take control of their educational process while helping educators transition from traditional teaching to facilitative roles.
🧠 Conceptual Foundation
1. Heutagogy Defined
Heutagogy (from Hase & Kenyon) emphasizes:
- Learner autonomy and capability development
- Reflection, self-efficacy, and adaptive learning
- Non-linear, personalized learning paths
This approach is particularly suited to computer science, where knowledge rapidly evolves and learners must continually adapt.
2. AI and Digital Pedagogy
Artificial Intelligence is positioned as a “digital augmentation”—a tool that amplifies human teaching rather than replaces it.
The dissertation argues that AI systems like VAITA can:
- Provide personalized feedback and guidance
- Encourage exploration and critical thinking
- Adjust learning materials based on student performance and learning style
💻 VAITA – The Virtual AI-Based Teaching Assistant
Core Functions
- Acts as a personal learning companion within an LMS or online course.
- Offers real-time responses, content recommendations, and adaptive challenges.
- Uses machine learning to identify learner gaps and suggest relevant topics.
- Encourages reflection and metacognition — learners learn how to learn.
Educational Role
- Supports heutagogical learning by promoting independent problem-solving.
- Allows educators to focus on mentorship and creativity, rather than routine instruction.
- Fosters a balance between human empathy and technological efficiency in education.
📘 Challenges in Modern Education
Grigoletti identifies systemic challenges that VAITA seeks to address:
- Rapid technological change in fields like AI and robotics, requiring constant upskilling.
- Rigid LMS platforms that restrict creative exploration.
- Overemphasis on standardized testing and passive knowledge consumption.
- Underdeveloped digital literacy among educators and learners.
He proposes that teachers should evolve into content designers, capable of creating dynamic, modular, and AI-integrated learning environments.
🌐 Key Findings and Arguments
- Heutagogical learning is essential in disciplines that demand lifelong learning.
- AI teaching assistants can personalize the educational experience, improving engagement.
- Combining AI adaptability with learner autonomy results in stronger educational outcomes.
- Educators must be retrained to act as facilitators and digital architects, not traditional lecturers.
💬 Educational Implications
- Shift in Power Dynamics: Students assume active responsibility for their learning.
- AI as Partner: VAITA serves as a learning ally that complements, not replaces, human teachers.
- Skill Integration: Emphasis on soft skills — creativity, critical thinking, collaboration, adaptability.
- Pedagogical Redesign: Traditional courses should evolve into intelligent, flexible ecosystems.
⚙️ Limitations
- The dissertation is largely theoretical, with limited empirical or technical validation.
- Implementation details of VAITA (AI architecture, data management, bias mitigation) are not fully developed.
- Ethical and privacy implications are acknowledged but not deeply explored.
🧾 Conclusion
Grigoletti’s research positions AI as a transformative force in higher education, capable of fostering self-determined, adaptive, and autonomous learners.
VAITA is envisioned not as a replacement for educators, but as a co-evolutionary partner that enhances learning through digital augmentation.
The work calls for a pedagogical revolution—moving from control and instruction to facilitation, creativity, and lifelong learning.
🏁 Overall Summary
| Category | Key Insights |
| Discipline | Computer Science Education |
| Approach | Heutagogical (self-determined learning) |
| Innovation | AI-based Virtual Teaching Assistant (VAITA) |
| Main Benefits | Personalized learning, autonomy, adaptability |
| Challenges Addressed | Rigid LMS, rapid tech change, educator adaptation |
| Limitations | Theoretical, limited empirical testing, ethical gaps |
| Contribution | Conceptual framework for integrating AI and heutagogy |
Daniel Grigoletti 