The technical core of the review traces how foundation models actually work and why they represent a break from earlier educational AI. These models are built on the transformer architecture, first described in 2017, which uses attention mechanisms to weigh relationships between every element of an input sequence. Pre-trained on vast corpora of text, images, audio, and video, models such as BERT, the GPT series, Llama, PaLM, and BLOOM acquire general-purpose capabilities that can then be adapted through instruction tuning and reinforcement learning from human feedback. The review emphasizes that this pre-training plus adaptation paradigm is what allows a single model to draft lesson plans, generate quiz questions, score essays, and explain mathematical reasoning, tasks that previously required separate, narrowly engineered systems.

Multimodality emerges as a particularly important trend for classrooms. Vision-language models such as BLIP-2, Flamingo, CogVLM, and MiniGPT-4 can interpret diagrams, handwritten work, and photographs of physical experiments, while audio-language models like Pengi and CLAP extend understanding to spoken language and sound. For younger learners who cannot yet type fluently, and for subjects like geometry, chemistry, and music where content is inherently visual or auditory, the review suggests that multimodal foundation models could finally deliver the long-promised vision of AI tutors that see and hear what a student sees and hears. Benchmarks such as CMMU and CMMMU, designed to test Chinese multimodal question understanding across school disciplines, illustrate how researchers are beginning to measure these capabilities against actual K-12 content.

Personalized learning stands out as the application with the most direct classroom impact. By combining foundation models with educational data mining and learning analytics, systems can model what an individual student knows, recommend learning paths, and adjust the difficulty and framing of explanations in real time. The review notes that recommender systems, long used to suggest resources in e-learning platforms, gain new power when a large language model can explain why a particular exercise was chosen, converse about a student’s confusion, and generate fresh practice items on demand. Research on concept-aware learning path recommendation and on bringing generative AI to adaptive learning points toward tutors that respond to the learner rather than forcing the learner through a fixed sequence.

Automated assessment is another area undergoing rapid change. The review surveys work showing that large language models can generate multiple-choice questions, reading comprehension exercises, and even full examination papers, with comparative studies finding that GPT-4-generated questions in programming education can approach the quality of human-crafted items. Essay scoring is advancing too, with researchers exploring how models can produce not just a grade but a rationale, mimicking the multi-trait judgments of human raters. Yet the authors are careful to flag the risks: studies questioning whether GPT-4 alone is sufficient for reliable essay grading, and concerns that generative AI undermines online exam integrity, show that automated assessment demands rigorous evidence-centered design and human oversight before it can be trusted at scale in schools.

For teachers, the review describes foundation models as collaborators rather than replacements. Systems for lesson planning can draw on decades of instructional design principles, generating plans aligned with established frameworks, while tools such as Tutor CoPilot demonstrate a human-AI approach in which the model supplies real-time expertise to the human tutor mid-session. Pre-service teachers studying AI-generated hints in online mathematics learning reported generally positive perceptions, suggesting that models can scaffold the difficult early years of teaching. The review also highlights domain-specific educational models such as EduChat, a chatbot purpose-built for intelligent education, as evidence that the field is moving beyond generic chatbots toward systems tuned to classroom norms and safety requirements.

The technical challenges the review catalogs are formidable. Hallucination, the tendency of models to produce fluent but false statements, is especially dangerous for children who lack the background knowledge to detect errors, motivating research such as the Woodpecker system for correcting multimodal hallucinations. Mathematical reasoning remains a known weakness, with studies probing whether ChatGPT truly understands place value and whether chain-of-thought prompting and self-consistency techniques can make multi-step problem solving reliable. Retrieval-augmented generation, which grounds model outputs in verified documents, and tool-augmented reasoning frameworks such as ReAct and ChatCot offer partial remedies, but the review stresses that benchmark results, including evaluations on MMLU and dedicated math benchmarks like MathEval, show performance varies widely across subjects and question types.

Equally important are the pedagogical and developmental questions. The review grounds its analysis in learning theory, invoking Piaget’s stages of cognitive development, Vygotsky’s zone of proximal development, Dewey’s experiential learning, and self-determination theory’s account of intrinsic motivation. A model tuned for adult self-learners may undermine a ten-year-old’s motivation by simply supplying answers, whereas productive failure research suggests students often learn more by struggling before receiving help. Age-appropriate instructional strategy, the review argues, is not a cosmetic layer but a fundamental design constraint: hints must be calibrated, reading levels matched to stages described in reading development research, and engagement fostered rather than eroded. The authors identify motivation and engagement as critical open issues that pure capability benchmarks do not measure.

Looking forward, the review sketches a research agenda for the coming years. It calls for rigorous evaluation of foundation models against curriculum standards, development of safeguards for child safety and data privacy, and hybrid workflows in which teachers retain pedagogical authority while models handle content generation, feedback, and administrative load. The global landscape it surveys, spanning OpenAI’s GPT-4, Meta’s Llama 3 family, Google’s Gemma, Alibaba’s Qwen series, and Chinese systems from Baichuan, ChatGLM, and iFLYTEK’s AutoSpark, indicates that educational AI is now an international race, with open-weight models lowering the barrier for schools and researchers to build customized tools. The authors’ central message is one of disciplined optimism: foundation models have demonstrated exceptional performance across domains, but realizing their promise in K-12 education will depend on sustained collaboration between AI engineers, learning scientists, and classroom teachers, ensuring that the technology serves the developing minds it is meant to support rather than the other way around.

Subject of Research: Applications of large-scale foundation models in K-12 education

Article Title: Current Trends and Future Prospects of Large-Scale Foundation Model in K-12 Education

Article References: Zhu, Q., Wang, M., Zhang, T., & Huang, H. (2025). Current Trends and Future Prospects of Large-Scale Foundation Model in K-12 Education. Frontiers of Digital Education, 2(2), Article 22. https://doi.org/10.1007/s44366-025-0059-6

Image Credits: AI Generated

DOI: 10.1007/s44366-025-0059-6

Keywords: foundation models, K-12 education, large language models, multimodal AI, personalized learning, automated assessment, educational technology, intelligent tutoring, hallucination, pedagogy, GPT-4, learning analytics