The study was conducted by Vassilis A. Failadis, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, and Vassilis P. Plagianakos of the University of Thessaly in Greece, and appeared online on September 11, 2026. Rather than surveying the sprawling field of artificial intelligence in education at large, the authors deliberately narrowed their focus to lesson planning, an essential component of a teacher’s daily workload and a core element of professional preparation. Following the search-appraisal-synthesis-analysis framework, known as SALSA, the researchers examined 53 recent publications and organized their findings around four themes: the quality and pedagogical value of AI-generated plans, the challenges and limitations of the technology, teachers’ attitudes and perceptions, and possible improvements and future directions. This focused lens matters, because lesson planning sits at the intersection of content knowledge, curriculum requirements, classroom context, and student needs, making it a revealing test case for what generative models can and cannot do.

The technical verdict on output quality is nuanced. Across the reviewed studies, large language models are frequently praised for producing well-organized plans with clear instructional formats. Platforms such as ChatGPT can reliably follow common instructional models, arranging activities into recognizable stages such as brainstorming, group discussion, guided exploration, presentation, and evaluation. In structural terms, the models have absorbed the conventions of lesson design from their training data and reproduce them fluently. Yet the empirical evidence consistently shows that these plans provide only a general framework and require substantial adaptation before they can function in a real classroom. The plans look like lessons; they do not yet behave like lessons, because they lack the embedded awareness of particular students, curricula, and teaching conditions that experienced educators bring to every plan they write.

Four recurring limitations emerge from the literature with striking consistency. The first is limited pedagogical depth: the teaching practices suggested by large language models tend to promote procedural knowledge rather than conceptual understanding, offering little support for complex learning processes such as reasoning, reflection, and justification. The second is weak differentiation, since the plans rarely include strategies for individualization or alternatives for students with different learning profiles, a shortcoming that becomes especially pronounced in specialized contexts such as special education. The third is unrealistic time planning. One study found that a plan designed by an AI for a single hour would require at least three hours in practice, with the models assigning very short durations to essential tasks while allocating generous time to reminder stages. The fourth is inconsistent output quality, with high variance reported even within identical prompt groups; outputs generated from the same prompt were found to differ by at least five rubric elements, a level of variability that undermines reliability in any professional workflow.

These findings lead the authors to an important practical conclusion about how teachers should interact with the technology. Producing a usable plan from a large language model is not a one-step process but an iterative one, in which the quality of the generated material depends partly on the clarity and specificity of the prompts. This reframes lesson planning with AI as a skill in its own right, blending prompt engineering with pedagogical expertise. A vague request yields a generic skeleton; a precise, context-rich prompt can yield something considerably more useful. But even the best outputs remain drafts that demand professional refinement, because no prompt can fully encode the tacit knowledge a teacher accumulates about how a particular class learns, stalls, and responds.

The review also paints a detailed picture of how teachers themselves perceive these tools. Most educators acknowledge the usefulness of artificial intelligence in lesson planning while simultaneously expressing reservations about its pedagogical adequacy. Teachers commonly use AI to generate content, yet many still prefer to work collaboratively with colleagues and emphasize the need for clear institutional policies to regulate its use. Some see the models as a helpful aid but do not believe they can replace a teacher’s pedagogical judgment; many agree that AI can assist with the initial organization of a lesson while stressing that the final responsibility for adapting and applying the plan lies with the teacher. Intriguingly, the authors observed a generational divide: younger educators tend to be more open to using AI, while more experienced teachers remain cautious, a difference the researchers attribute to the influence of professional experience and familiarity with classroom practice on teachers’ perspectives.

To move the field forward, the review identifies four main directions that appear repeatedly in the literature. The first is teacher training and AI literacy, ensuring that educators understand what these systems can actually do and develop the ability to adapt and assess the content they produce. The second is prompt engineering and reflective use, since the pedagogical value of generated content depends directly on how clear, precise, and well-targeted the prompts are. The third is policy development and ethical guidelines, with clear rules needed to guarantee responsible and educationally appropriate deployment of AI tools in schools. The fourth is model improvement and pedagogical alignment, calling for enhanced features and greater flexibility in instructional design, including guided interfaces and options that allow teachers to supply contextual information about their students, curriculum, and constraints. Together, these directions sketch a roadmap in which the technology and the profession evolve in tandem rather than in competition.

The review is equally candid about what the research itself still does not know, identifying four significant gaps. Most existing studies remain theoretical or rely on teachers’ opinions without examining what happens when AI-generated plans are actually applied in classrooms. Most are built on qualitative observations, interviews, or questionnaires, with limited direct comparisons between teacher-created and machine-generated plans. The literature is also geographically concentrated, with the United States and Türkiye appearing most frequently, raising questions about how findings transfer to other educational systems. Finally, little attention has been paid to the processes through which teachers interact with AI systems during planning, leaving the human-machine workflow largely unexplored. Recent empirical work nonetheless offers telling hints: one study found that although teacher-created plans were rated higher in quality overall, teachers were unable to reliably distinguish them from AI-generated ones, while another found that refined AI-generated mathematics lesson plans received high evaluation scores and in several cases outperformed teacher-created plans, even though teacher-generated procedures aligned more closely with actual classroom practice. The gap between perceived and actual quality is clearly a live scientific puzzle.

The overall conclusion is measured rather than sensational. Large language models can serve as useful tools for lesson planning, but they are not yet capable of replacing the teacher’s pedagogical role. Their outputs are usually well-structured, organized, and goal-oriented, yet they tend to fall short in adaptation, differentiation, and pedagogy, the dimensions that determine whether a plan actually teaches anyone anything. As the authors put it, these systems should currently be viewed as support for lesson planning rather than as ready-made planning tools, and should not yet be considered a substitute for pedagogical expertise; their use makes sense when they function as support tools rather than as replacements for professional judgment. For a technology often portrayed as poised to automate teaching outright, the evidence points to a more grounded future: AI as a fast, tireless drafting assistant, and the teacher as the indispensable editor who knows exactly which students are sitting in the room.

Subject of Research: The use of large language models for lesson planning in education and their pedagogical limitations

Article Title: Large language models in lesson planning: Useful aid, but not yet ready to replace teachers

Article References: Large language models in lesson planning: Useful aid, but not yet ready to replace teachers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: large language models, lesson planning, generative AI, ChatGPT, education technology, pedagogy, teacher attitudes, prompt engineering, AI literacy, narrative review, differentiation, Frontiers of Digital Education