Reducing Teacher Burnout Through AI Integration

This project represents a comprehensive formative assessment designed to validate and optimize an instructional solution addressing one of education's most pressing challenges. With teacher burnout affecting nearly 50% of educators within their first five years, this mixed-methods evaluation examined whether AI integration training could genuinely reduce stress rather than contribute to "technostress."

Through strategic survey deployment to 37 educators across diverse educational contexts, the project analyzed the gap between solution design and real-world user needs. The evaluation uncovered critical insights about educator preferences, pricing sensitivity, and implementation barriers, ultimately producing evidence-based recommendations that could dramatically improve adoption rates and effectiveness. This project demonstrates the power of user-centered evaluation in transforming potential market failures into competitive advantages through data-driven design optimization.

Problem Statement

Teacher burnout has reached crisis levels in education, with nearly 50% of teachers leaving their roles within five years. While technology promises to streamline workflows and reduce stress, many educators experience "technostress" from poorly implemented AI and digital tools that add complexity rather than relief. The challenge was to validate whether an AI integration course could genuinely address teacher burnout while identifying specific design modifications needed for successful implementation.

My Role & Process

As the primary researcher and evaluator, I conducted a comprehensive mixed-methods formative evaluation to assess the viability of "Reducing Teacher Burnout Through AI Integration."

I designed and deployed a strategic survey to 37 educators across diverse grade levels (Pre-K to postsecondary) and experience ranges (0-2 years to 11+ years).

My process included quantitative analysis of educator challenges and preferences, qualitative theme extraction from open-ended responses, and convergent analysis to identify alignment between data sources.

I then synthesized findings into actionable strategic recommendations for solution optimization.

Tools and Methods Used

  • Mixed-methods survey design and distribution via Google Forms

  • Quantitative frequency analysis for challenge identification and resource preferences

  • Qualitative coding and theme extraction for deeper contextual insights

  • Convergent analysis methodology to identify data alignment and gaps

  • Strategic recommendation framework based on evidence-based findings

  • Professional evaluation reporting following industry standards

Outcomes and Results

The evaluation confirmed strong market validation with 45.9% of educators identifying burnout as their primary challenge. However, analysis revealed six critical misalignments between the original solution design and user needs.

Key Findings

Key findings included preference for efficiency-focused positioning over technology-focused framing, demand for freemium pricing models (43% preferred $0-25), and need for microlearning formats rather than traditional course structures.

The evaluation produced concrete strategic recommendations that could improve projected adoption rates from 45-60% to 80%+.

Presentation for Stakeholders

Lessons Learned

This evaluation reinforced the importance of user-centered design validation before full development. I learned that educators are highly receptive to innovation when positioned as stress-reduction tools rather than additional technology training. The convergence between quantitative priorities and qualitative themes provided robust validation of findings.

Most importantly, I discovered that formative evaluation can transform potential barriers into competitive advantages when recommendations are implemented strategically. This experience strengthened my ability to translate complex data into actionable business insights.