At the heart of CanopyLAB’s generative AI capabilities lies AICATO, their advanced AI engine. AICATO, which stands for Adaptive Intelligence for CanopyLAB’s Adaptive Teaching and Learning Objects, plays a pivotal role in creating personalized learning experiences for users across CanopyLAB’s platforms. Some of our capabilities are:
Generative AI and Automated Content Generation
One of the key benefits of generative AI in CanopyLAB’s platform is its ability to generate learning content. We leverage GenAI and Natural Language Processing (NLP) to achieve this. We can actually generate entire unit or module descriptions for your courses, as well as learning journals as quizzes. In fact, we save 71 % of a learning designer’s course creation time through automated course generation, when we compare those that use these AI functionalities with those that prefer building everything from scratch.
Generative AI and Intelligent Recommendations (closed BETA test only)
CanopyLAB’s AI engine takes personalized learning a step further by providing intelligent recommendations. By considering a user’s learning history, preferences, and areas of struggle, the AI engine can suggest additional resources or activities reinforce understanding. For instance, if a learner faces challenges with a particular concept, the AI engine might recommend supplementary materials or interactive exercises to help them overcome the difficulty. This functionality is currently being BETA tested with selected customers.
By considering a user’s learning history, preferences, and areas of struggle, the AI engine can suggest additional resources or activities reinforce understanding. For instance, if a learner faces challenges with a particular concept, the AI engine might recommend supplementary materials or interactive exercises to help them overcome the difficulty.
Generative AI and Adaptive Assessments
Traditional assessments can often feel static and one-size-fits-all, but CanopyLAB’s generative AI engine enables the creation of adaptive assessments. These assessments dynamically adjust the difficulty level based on a user’s performance, ensuring that learners are appropriately challenged. By tailoring the assessments to an individual’s proficiency, CanopyLAB fosters a more effective learning experience that encourages continuous improvement. Please check out our different posts on our three adaptive quizzes:
Automated Qualitative Feedback (closed BETA test only)
CanopyLAB’s AI engine incorporates Natural Language Processing (NLP) to provide users with personalized feedback on their written responses. By analyzing writing style, grammar, and syntax, the AI engine offers feedback that is specific to the learner’s abilities, enabling them to enhance their writing skills and develop confidence in effective communication. This feature contributes to a comprehensive learning experience that extends beyond mere content consumption.
By analyzing writing style, grammar, and syntax, the AI engine offers feedback that is specific to the learner’s abilities, enabling them to enhance their writing skills and develop confidence in effective communication.
Automated grading (closed BETA test only)
As a foundation for our automated qualitative assessment, we first built Automated grading. Automated grading isn’t new, but it has revolutionized the way educators assess student work. By leveraging advanced technologies, such as machine learning and artificial intelligence, automated grading streamlines the evaluation process, saving time and effort for both teachers and students. This innovative approach allows LMS platforms to automatically score assignments, quizzes, and exams based on predefined criteria, providing quick feedback to learners. Not only does automated grading expedite the assessment process, but it also ensures consistency and fairness in evaluating student performance. By removing the subjective element from grading, LMS automation enables educators to focus on personalized instruction and student engagement, fostering a more efficient and effective learning environment.
