Proof pending. Core topic summary fields are still materializing.
The integration of AI in education is transforming teaching and learning processes, with applications ranging from AI teaching assistants to intelligent tutoring systems. Recent studies demonstrate the effectiveness of AI tools in enhancing student engagement and understanding, particularly in programming and mathematics. For instance, AI-driven assessments can provide scalable feedback and verify students' comprehension, while personalized problem generation systems allow educators to tailor tasks to individual learner needs. However, challenges such as AI dependency among students and the need for robust pedagogical frameworks remain. Addressing these issues is crucial for educators and developers to ensure that AI tools support rather than undermine essential academic skills. As educational institutions adapt to these innovations, the focus must be on balancing technological advancements with pedagogical integrity to foster effective learning environments.
Topic-specific paper and score movement from the daily diff ledger.
Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work evaluates pedagogical quality via answer leakage-...
Large Language Models (LLMs) challenge conventional automated programming assessment because students can now produce functionally correct code without demonstrating corresponding understanding. This ...
The double-edged sword of integrating Large Language Models (LLMs) requires an effective triadic collaboration mechanism among LLMs, teachers and students, especially for K-12 education. By developing...
The rapid rise of LLMs over the last few years has promoted growing experimentation with LLM-driven AI tutors. However, the details of implementation, as well as the benefit in a teaching environment,...
Large Reasoning Models (LRMs) benefit substantially from training on challenging competition-level questions. However, existing automated question synthesis methods lack precise difficulty control, in...
Large Language Models (LLMs) are increasingly used in math education not only as problem solvers but also as assessors of learners' reasoning. However, it remains unclear whether stronger math problem...
The increasing dependency among Filipino college students on artificial intelligence (AI) poses concerns about the potential decline of fundamental academic competencies. This study examines the exten...
To enhance LLMs' impact on math education, we need data on their mathematical prowess and biases across prompts. To fill this gap, we introduce MEDS (Math Education Digital Shadows) as a dataset mappi...
Despite emerging use in Indonesian classrooms, there is limited large-scale, teacher-centred evidence on how AI is used in practice and what support teachers need, hindering the development of context...
Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-the-loop system for personalizing middle school ma...
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Canonical route: /topics
Agent Handoff
Canonical ID ai-in-education | Route /topic/ai-in-education
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/ai-in-educationMCP example
{
"tool": "search_papers",
"arguments": {
"query": "AI in Education",
"cluster": "AI in Education"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "AI in Education",
"normalized_query": "ai-in-education",
"route": "/topic/ai-in-education",
"paper_ref": null,
"topic_slug": "ai-in-education",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.