by AI Tutoring Platform announced that its core learning system has entered the second phase of engineering polishing, covering modules such as task rhythm adaptation, multimodal content adaptation, PoL on-chain verification, and graph-driven path generation, and is committed to building a verifiable, sustainable, and collaborative decentralized intelligent learning infrastructure. The project is not yet online, and all systems are still in the testing phase. The official ecological cooperation pre-access channel is also opened.
July 2025 · From AI Tutoring Labs Inc.
As the global education digitalization process continues to advance and AI content generation technology evolves rapidly, a large number of learning platforms are still stuck in the content distribution and check-in system, and learners’ paths are limited to linear courses, static tasks, and “percentage of completion” driven mechanisms. The key to learning effectiveness – behavioral rhythm, cognitive absorption ability, and understanding process – has never been systematically modeled or verified.
AI Tutoring Platform is changing all this.
As the world’s first decentralized intelligent learning system built on the Proof of Learning (PoL) consensus mechanism, AI Tutoring does not attempt to become another content platform or AI instructor assistant, but is committed to building a complete intelligent learning engine from rhythm control, task scheduling, feedback loop to on-chain verification closed loop.
The project is currently in the second phase of engineering testing. All functions have not yet been launched, but the polishing and internal testing of the following key structural modules have been completed:
Task scheduling system driven by learning behavior
The platform does not rely on a fixed course rhythm, but automatically adjusts the task length, difficulty and time window by real-time monitoring of learners’ behavioral data (focus fluctuations, repeated operations, pauses in thinking, etc.), so that each section of learning is in a sustainable absorption state, minimizing “passive completion” and “fatigue collapse”.
Reinforcement feedback mechanism (Reinforcement Loop)
Every operation, an error retry, and an interruption behavior will be regarded as a signal for the system to update the strategy. The platform is testing a set of reinforced strategy feedback networks that can fine-tune the learning path in real time, so that the content is not “recommended to you”, but “dynamically negotiated with your current cognitive state”.
Multimodal content adaptation engine
Different learners have very different ways of understanding. The platform has designed a content adaptation system based on behavioral preferences, which can automatically switch or merge in multiple forms such as text, illustrations, videos, simulators, etc., to achieve true “learning mode perception”, thereby optimizing absorption efficiency rather than stacking information density.
Knowledge graph-driven path reconstruction engine
The platform has completed the knowledge structure modeling system, organized all knowledge units in the form of concept dependency graphs, and updated the mastery status of each learner in real time. The system no longer issues tasks by module, but identifies your blind spots and potential areas, and generates structural path recommendations.
Rhythm-adaptive PoL verification mechanism
In the PoL core module, AI Tutoring emphasizes not only “what has been learned”, but “whether it is truly understood”. The platform uses mechanisms such as behavioral entropy analysis, natural interaction duration, and cognitive delay matching to achieve trusted on-chain verification of the real learning process under non-linear rhythm, effectively blocking mechanical brushing tasks and non-human operation paths, and ensuring the authenticity of incentive distribution.
Education is not content, but structure
The AI Tutoring team said that they are not in a hurry to launch functions, but hope to enter system testing and ecological joint debugging after the “logical closure and engineering strength” of the functional modules are completed.
“The learning system is not a content factory, it should be a cognitive operating system.”
“We are not building a product, but repairing a set of underlying logic for future education.”
Cooperation invitation and follow-up arrangements
The project is currently opening the pre-connection window for the following cooperation directions:
Education content platform, course provider and certification agency
Decentralized identity system and on-chain certificate protocol
Web3 education project party and knowledge certificate collaboration party
Wallet system, learning SBT, KYC compliance module provider
RWA learning achievement assetization scenario connection agency
The official will announce the API access details, PoL verification standard document, path adaptation logic structure description in the future, and release the roadmap update at an appropriate time.
Media Contact
Organization: AI Tutoring
Contact Person: James Anderson
Website: https://aitutoring.ink/
Email: Send Email
Country:Singapore
Release id:30631
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