Teaching

Digitalized Augmentations for Heutagogical Computer Science Learning Using Virtual AI-based Teaching Assistant (VAITA)

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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:

  1. Rapid technological change in fields like AI and robotics, requiring constant upskilling.
  2. Rigid LMS platforms that restrict creative exploration.
  3. Overemphasis on standardized testing and passive knowledge consumption.
  4. 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

CategoryKey Insights
DisciplineComputer Science Education
ApproachHeutagogical (self-determined learning)
InnovationAI-based Virtual Teaching Assistant (VAITA)
Main BenefitsPersonalized learning, autonomy, adaptability
Challenges AddressedRigid LMS, rapid tech change, educator adaptation
LimitationsTheoretical, limited empirical testing, ethical gaps
ContributionConceptual framework for integrating AI and heutagogy

EDU800 Week 2 Annotation

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Labaree, D. F. (2003). The Peculiar Problems of Preparing Educational Researchers, Educational Researchers 32(4), 13–22.

Labaree explores how the practice of teaching, and teaching skills translate, can be developed into skills for educational researchers and how there are similar characteristics, especially the academic skills, between teachers and educational researchers. He gives strategies for filling the gap between the two when doctoral students pursue their degree. He compares and contrasts the two, including the commonality of academic discipline, discussing the cultural divide between researchers and teachers, and specifically the need for researchers to expand greatly, their world view, whereas teachers alone are limited to the subject matter, modes of delivery, and level of students which they teach. The researcher is unbridled by these limitations. He also compares how researchers in areas of knowledge such the social sciences, medicine and engineering differ from how educational researchers have to approach their subject as they utilize their analytical, theoretical and intellectual skills.

Since researchers are responsible for constructing new knowledge, and teachers are focused on knowledge dissemination, we see the common threads and the overlap between the two, especially since many teachers, once having achieved their doctoral degrees, become researchers. Since the teachers are steeped in the business of education, they naturally can relate to the topic of educational research. Teachers, regardless of the level which they instruct, have to have intimate knowledge of the educational process, therefore they are researching every day that they teach. It almost becomes a instance of the chicken-or-the-egg problem, in which, teachers need to research their subject matter in order to effectively evolve, common knowledge and literacy of the student, and as a researcher, we must push the envelope of existing knowledge and break out new knowledge, of which will become that which is taught.

This article gives ammunition to the student of Educational Technology, in that it outlines various ways to transition from teacher to educational researcher. However, it may also help with forming a cyclical process of going from teacher to educational researcher to teacher, etc. It doesn’t matter what subject matter a doctoral student of educational technology will eventually be involved with, but the process of researching will be ingrained in the subject matter expert, such as Business, or Biology, or Computer Science. The information contained in this article will also help the student of learning science with strategies to both quantitatively and qualitatively analyze the field of education from within and without, in order to be a more effective instructor.