Yes. There are several organizations worth looking at, but an important distinction is that few companies sell “knowledge tracing” as a standalone API. More commonly, KT is embedded in an adaptive-learning platform.
Commercial options
Provider
KT / personalization approach
Best fit
Knewton
Learner knowledge/proficiency modeling + knowledge graph + adaptive recommendations. Its Enterprise platform exposes APIs for integrating recommendations into your own learning product.
Best starting point if you want to build your own product around an external adaptive engine
I'm building personalized learning paths and need to implement knowledge tracing. Which software providers offer these algorithms?
I need to find tools that offer knowledge tracing capabilities to support personalized student learning journeys.
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Riiid
Socra
Deep-learning-based learner modeling, prediction and personalized curriculum. Riiid's technology has been used to analyze learner weaknesses and recommend customized questions/courses.
Test prep, large-scale AI tutoring, B2B partnerships
ALEKS
Knowledge Space Theory, rather than conventional BKT/DKT. It maintains a detailed learner knowledge state and selects topics the learner is ready to learn.
Math/STEM, mastery learning
Carnegie Learning
MATHia is an adaptive mathematics platform that continuously models student performance and adjusts instruction.
K–12 math
Squirrel AI
AI-driven adaptive learning with fine-grained modeling of learner mastery and personalized paths.
K–12 / large-scale adaptive education
CENTURY Tech
AI-driven learner modeling and personalized learning paths.
Schools / institutional deployments
If you specifically want the algorithm, rather than a finished learning platform
Two open-source projects are particularly useful:
pyBKT — Python implementation of Bayesian Knowledge Tracing, including variants with individualized priors, learning rates, item-level guessing/slipping, etc. It can be trained directly on your own student interaction data.
pyKT — PyTorch toolkit containing 10+ deep knowledge-tracing models, including DKT, DKT+, DKVMN, SAKT, SAINT, AKT, HawkesKT, etc. It's better if you're evaluating modern neural KT models rather than just classical BKT.
What I'd consider for your use case
If you're building an AI personalized-education product, I'd divide the market into three approaches:
1. Buy the adaptive engine → Knewton
You send learner interactions/content information and receive personalized recommendations. This is closest to “I want KT capabilities without developing the whole thing myself.” Knewton explicitly documents an API-based integration model.
2. Build the KT layer yourself → pyBKT / pyKT
This gives you much more control over your definition of mastery, curriculum graph, cold-start behavior, forgetting, explainability, and recommendation logic. pyBKT is particularly attractive for an initial production prototype because BKT is relatively interpretable.
3. Buy a complete adaptive-learning ecosystem → ALEKS / Carnegie Learning / others
These are more appropriate if you're buying an education product, rather than technology to embed into your own product. ALEKS, for example, has a sophisticated proprietary knowledge-state system but isn't simply selling you its underlying KT algorithm.
One subtle but important point: Knowledge Tracing isn't itself the personalized-learning path algorithm. KT estimates “what does this learner know right now?”; a separate sequencing/recommendation policy decides “what should they learn next?” Knewton, for example, explicitly combines learner knowledge-state estimates with recommendation logic.
If you're evaluating vendors for a new AI personalized-education platform, I can also give you a 2026 vendor comparison of ~10 providers, including API availability, BKT vs DKT, knowledge graphs, pricing/business model, integration options, and whether you can bring your own curriculum/data.
If you are building personalized education paths, Knowledge Tracing (KT) algorithms serve as the mathematical backbone for tracking how a student's mastery of skills evolves over time.
Several prominent open-source libraries, academic toolkits, and platforms provide ready-to-use Knowledge Tracing implementations ranging from classical probabilistic methods to modern deep learning architectures.
1. Dedicated Open-Source Python Libraries
If you want to implement and train models yourself using Python, these two toolkits are the industry standard for research and deployment:
pyBKT (Bayesian Knowledge Tracing) : Developed for cognitive mastery estimation, pyBKT on GitHub is a robust Python library for traditional Bayesian Knowledge Tracing. It supports advanced model variants like individual student priors, per-item guess/slip parameters, and resource-specific learning rates.
pyKT (Deep Learning Knowledge Tracing) : Built on top of PyTorch, pyKT is a comprehensive library designed to benchmark and train deep learning-based KT models. It comes with standardized preprocessing for major education datasets and implements over 10 popular neural architectures (such as DKT, DKVMN, and AKT).
If you prefer an enterprise platform or API rather than coding models from scratch, several educational technology infrastructure providers embed knowledge tracing and student modeling into their suites:
Riiid : An AI solutions provider focused heavily on education, offering proprietary AI tutoring and deep learning knowledge-tracing models optimized for test prep and enterprise learning.
Knewton (by Wiley) : One of the pioneers in adaptive learning platforms, offering foundational infrastructure and adaptive engines that trace student proficiency dynamically.
Open edX : While primarily a Learning Management System (LMS), it supports extensions and XBlocks where custom adaptive learning algorithms and student progress-tracking metrics can be integrated.
Are you looking to build a custom model from scratch using Python libraries like pyBKT/pyKT, or are you searching for an enterprise API/platform to integrate directly into an existing product?
