Enterprise AI Analysis: Generative AI in Education
Navigating the Future of Education with AI-Enhanced Physical Computing
This research explores practical applications of generative AI and large language models (LLMs) within physical computing and project-based learning (PBL) in K-12 settings, aiming to enhance existing pedagogical approaches and address the growing impact of AI on computing education.
Executive Impact
Generative AI presents a transformative opportunity to revolutionize K-12 physical computing education, improving student engagement, efficiency, and accessibility in project-based learning.
0%
Improved Student Engagement
0%
Reduced Project Setup Time
0%
Increased Accessibility
0%
Enhanced Problem-Solving
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Foundation of Physical Computing & PBL
Physical computing combines hardware and software to create interactive systems, providing a hands-on approach to learning programming and computational thinking. It bridges theory and practice, allowing students to engage with real-world applications beyond screen-based coding. Project-based learning (PBL) provides meaningful contexts for learners to apply their skills and deepen understanding.
Critical Success Factors for PBL in Computing Education (Pucher & Lehner):
- Problems in early project phases due to students' lack of project management skills.
- Student motivation ranging from very low to extremely high.
- Teacher-student interactions varying with experience and comfort levels.
- The origin of the project or project idea.
This PhD research aims to explore whether generative AI LLMs can help address these factors.
Early Research Focus
The initial stages of this research are focused on two key areas:
- Understanding how generative AI large language models (LLMs) are currently being used in computing education through a review of existing literature.
- Examining how physical computing is integrated into K-12 computing education across the UK.
Later Research Phases Aim
Investigate LLM support for design, construction, troubleshooting, documentation
→
Examine LLM/PBL impact on educational inequities
→
Explore student perception & engagement with AI tools in hands-on activities
→
Develop practical frameworks for educators
Key Contributions of this PhD Research
This PhD research aims to contribute to the field of computing education by:
- Developing evidence-based insights into how generative AI LLMs can, or cannot, support physical computing and project-based learning (PBL) in K-12 settings.
- Offering practical pedagogical guidance for educators.
- Proposing a framework for hands-on computing activities in an age of AI.
- Exploring how these technologies can improve accessibility and engagement.
By focusing on both student and educator experiences, the research seeks to inform more inclusive, creative, and sustainable approaches to computing education.
Research Methodology Flow
Exploratory study (Ethnographic observations & Focus groups)
→
Examine physical computing & PBL in K-12 (student experience)
→
Follow-up study (Educator perspectives on opportunities & challenges)
→
Reflexive thematic analysis (Identify key themes across groups)
→
Co-develop practical framework (Longitudinal intervention with educators)
Study Design
The study begins with an exploratory phase, utilizing ethnographic observations and focus groups to understand current practices of physical computing and PBL in K-12 environments from the student's perspective. This will be followed by a study gathering educators' insights on the instructional opportunities and challenges presented by these approaches. The collected data will undergo reflexive thematic analysis to identify common themes across both student and educator groups, laying the groundwork for co-developing a practical framework for educators through a longitudinal intervention.
Project Impact Calculator
Estimate the potential efficiency gains and resource reclamation by integrating AI into your educational computing projects.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Research Implementation Roadmap
A phased approach to integrate these research insights into practical educational settings.
Phase 1: Literature Review & Current Practices Assessment
Systematically review existing literature on LLMs in computing education and assess current K-12 physical computing integration across the UK. This phase sets the baseline understanding.
Duration: Months 1-3Phase 2: Exploratory Field Study
Conduct ethnographic observations and student focus groups in K-12 settings to understand the practical implementation of physical computing and PBL, focusing on student experiences.
Duration: Months 4-8Phase 3: Educator Perspectives & Data Analysis
Gather insights from educators via interviews/focus groups on AI integration challenges and opportunities. Perform reflexive thematic analysis on all collected data (student and educator).
Duration: Months 9-12Phase 4: Framework Co-development & Intervention
Collaborate with educators to design and refine a practical framework for integrating AI into physical computing PBL. Implement and evaluate this framework through a longitudinal intervention.
Duration: Months 13-18+Ready to Transform Your Educational Programs with AI?
Ready to discuss AI's role in your educational programs? Schedule a personalized strategy session to explore how these insights can be tailored to your institution's goals.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat