If you’re an existing customer, then your Customer Success Manager can bring you through the available pricing options. If you’re not yet a customer, then get in touch with us to learn more about licensing Learnosity.
With Feedback Aide, your own standards-aligned rubrics guide AI auto-grading. Unlike black box AI systems, Feedback Aide provides clear scoring decisions—dramatically speeding up grading while ensuring educators define the evaluation criteria.
With a choice between standard or advanced AI scoring models, Feedback Aide gives you the flexibility to meet your needs. And because Learnosity runs infrastructure for 150+ major clients, we can pass on economies of scale—so you can deliver next-gen grading experiences at a fraction of the cost.
Feedback Aide spots opportunities for learning growth and generates constructive feedback tailored to each student. Teachers also have total freedom to customize feedback based on their own insights.
Feedback Aide achieves a Quadratic Weighted Kappa (QWK) score of 0.88 on K-20 essays—meaning its performance is on par with a human grader. Acting as a super-powered assistant to educators, Feedback Aide performs a rapid and reliable first pass that’s then manually validated.
As an API-based solution, Feedback Aide integrates seamlessly into your existing learning platform and delivers a user experience that’s tailor-made for educators. Built on enterprise-grade infrastructure with an uptime of 99.95%, this AI-powered grading solution delivers consistent performance—even under peak traffic loads.
Learn more from the experts behind our AI-enriched assessment solutions.
If you’re an existing customer, then your Customer Success Manager can bring you through the available pricing options. If you’re not yet a customer, then get in touch with us to learn more about licensing Learnosity.
" } [1]=> array(2) { ["title"]=> string(30) "How accurate is Feedback Aide?" ["content"]=> string(607) "In our testing and validation, we’ve found that our engine typically scores essays the same as, or within a few points of, teacher-scored essays in our data sets.
To conduct this evaluation, we sourced publicly available datasets of essays with professional scoring, as well as data from clients and industry partners. We then compared professional human scores to AI-generated scores.
We continue to evaluate the performance of our engine against multiple datasets.
" } [2]=> array(2) { ["title"]=> string(42) "Is it just an LLM? Or are you training it?" ["content"]=> string(369) "We’re building on top of large language models (LLMs), which allows us to optimize for grading use cases while still benefiting from the rapid advancements in generative AI.
We use different LLMs for different use cases, tailored to meet the specific needs of grading tasks.
" } } [1]=> array(3) { [0]=> array(2) { ["title"]=> string(38) "Will our data be used to train models?" ["content"]=> string(616) "No, your data will not be used to train black box models.
For our release, we’ll be using LLMs through key enterprise providers with whom we have full contractual relationships and data processing agreements in place. This is the same approach we use for Author Aide, with contractual commitments that ensure nothing provided via the LLM APIs is used for training purposes.
" } [1]=> array(2) { ["title"]=> string(40) "How specific does the rubric need to be?" ["content"]=> string(276) "We’ve validated the system with both general rubrics (e.g. for analytic or expository essays) and more specific rubrics tailored to individual assignments. More detailed and specific rubrics will generally yield better results.
" } [2]=> array(2) { ["title"]=> string(25) "What about short answers?" ["content"]=> string(280) "This is an exciting area where Feedback Aide could offer significant value, and we’re currently exploring it. At the moment, Feedback Aide is capable of grading shorter essays (around 100 words) when used with more concise rubrics.
" } } }