Introduction: the problem of timely feedback
Teachers today face ever-larger classes and tight deadlines. Providing each student with personalized feedback is time-consuming and often insufficient to keep pace with the rapid advances in information. New generations of AI search agents, such as Nace AI’s recent Drex, are changing this landscape by offering real-time information retrieval and multimodal analysis that integrates directly into teaching workflows.
Why AI search agents matter for education
More than simply querying a single database, search agents explore multiple sources, assess authority, and summarize results in seconds. This enables teachers to:
- Retrieve up-to-date resources (e.g., the latest 2026 climate-change studies) without getting lost in manual research.
- Customize examples to each learner’s proficiency level.
- Generate comments aligned with ministerial curricula thanks to structured prompts.
The d3-ultra multimodal model from vLLM Semantic Router, an open 27B decision model that processes text, images, and video in a single pass, adds a level of visual and practical understanding that was previously unthinkable for tutoring systems.
Building a prompt for a search agent
A well-crafted prompt is the first step. The following example uses minimal syntax that can be adapted to any agent-based search engine, including those that leverage the Drex search engine.
# Search agent prompt: extract up-to-date resources
prompt = """
Retrieve the latest information (2026) on {topic} from reliable academic sources.
Provide a bibliographic-style summary with links and access dates.
"""Replacing{topic}with a keyword such as “artificial photosynthesis techniques” makes the agent return abstracts, DOIs, and a reliability score. Teachers can then embed these citations into lesson materials with a few clicks.
Use multimodal models to analyze assignments
When a student submits an assignment that includes code, diagrams, or a short video, d3-ultra can analyze each modality in a single call. The result is an evaluation that considers not only textual correctness but also visual clarity and algorithmic efficiency.
Example prompt for programming assignment feedback
# Prompt for d3-ultra: analyze student task and generate feedback
prompt = """
Analyze the following student task: {task_text}
Check correctness, clarity, performance optimization, and best practices.
Provide structured feedback: strengths, areas for improvement, concrete suggestions.
Use {level}-level language and a {tone} tone.
"""Feeding a student’s code into the model flags hidden inefficiencies (e.g., suboptimal cache use), suggests stylistic improvements, and links each tip to an up-to-date resource pulled by the search agent.
Speed up the process with caching (AKV)
Tsinghua’s open-source AKV serving system, built on SGLang, lets models keep an active KV-cache that can be reused for similar tasks. For a teaching environment, this means frequent prompts (e.g., “generate a 500-word essay correction sheet”) can be served almost instantly, cutting total feedback latency from minutes to seconds.
A practical pattern for teachers is:
- Identify the five most-used prompts in your workflow.
- Load them into an AKV instance with a session key “teacher_{id}”.
- When a new assignment matches one of these templates, retrieve the response from the cache and only then enrich it with fresh data from the search agent.
Complete 5-step workflow
- Define the learning objective
- Use a search agentto pull up-to-date abstracts and reliable statistics (Drex).
- Send the assignment to d3-ultrafor multimodal analysis (text + any student-provided figure).
- Combine results
- Review and publish
Key takeaways
- AI search agents (like Drex) automate the retrieval of up-to-date information, freeing time for human interaction.
- Multimodal models (d3-ultra) deliver evaluations that go beyond grammar, covering visual and logical correctness.
- Strategic caching with AKV dramatically reduces latency, making real-time tutoring feasible even for large classes.
- Well-structured prompts, combined with search agents and caching, produce feedback that is consistent, personalized, and curriculum-aligned.
- Implementing this stack today positions schools as innovation labs for 2026 and beyond.
Leveraging 2026 AI breakthroughs
Example prompt that fuses OCR, search, and multimodal analysis
Conclusion: the future of tutoring is already here
Today’s AI tools are no longer just “ask-and-answer” assistants: they are proactive partners that can sift through the web, analyze multimodal assignments, and serve answers in milliseconds thanks to advanced caching solutions. By integrating search agents, multimodal models like d3-ultra, and optimized serving systems such as AKV, teachers can deliver personalized feedback that was once unthinkable while maintaining a meaningful human connection with each learner.
The next step is simple: experiment with a search prompt for a class topic, try a multimodal analysis on an existing assignment, and set up an AKV cache for your most-used prompt. Within days you’ll see feedback quality improve and administrative workload drop dramatically.