Assessing the Impact of AI-Generated Personalized Lesson Plans on Teacher Burnout and Student Engagement

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@article{1714511,
      author = {Gopal Singh},
      title = {Assessing the Impact of AI-Generated Personalized Lesson Plans on Teacher Burnout and Student Engagement},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {2190-2195},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714511.pdf},
      abstract = {The global educational landscape currently confronts a dual crisis: a systemic decline in teacher retention due to professional exhaustion and a fluctuating level of student investment in standardized curricula. The emergence of generative artificial intelligence (GenAI) offers a transformative mechanism to address these challenges through the automation of personalized lesson planning. By shifting the paradigm from a "one-size-fits-all" instructional model to an adaptive, data-driven framework, educational systems are attempting to reclaim teacher time and optimize student cognitive involvement. This report evaluates the empirical evidence and theoretical underpinnings surrounding the integration of AI-generated personalization, focusing on its capacity to mitigate burnout and catalyze multi-dimensional engagement. Drawing on Job Demands-Resources (JD-R) theory, Conservation of Resources (COR) theory, and Self-Determination Theory (SDT), this paper analyzes recent implementation data from 2023–2025. Key findings indicate that AI tools can reclaim an average of 5.9 to 7 hours per workweek for educators, significantly reducing emotional exhaustion. Furthermore, AI-driven personalization is associated with a 54% increase in student test scores and a 10-fold increase in engagement levels. However, the report also addresses critical ethical challenges, including professional de-skilling, algorithmic bias, and the "augmentation paradox" in student learning.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I3-1714511}
  }