An AI Teaching Assistant Rooted in 3 Pedagogy Pillars

Artificial Intelligence can make education more accessible, personalized, and supportive. But technology alone isn't enough. To truly enhance learning, AI must be grounded in sound pedagogical principles. That's why we designed our AI teaching assistant with a deep understanding of how students learn best, drawing on established learning theories and frameworks.
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Our Goal:
To create an AI assistant that doesn't just provide answers but fosters a deeper, more meaningful learning experience for every student.
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The Challenge:
Traditional educational models often struggle to cater to the diverse needs of individual learners. Students may have different learning styles, paces, and levels of prior knowledge. One-size-fits-all approaches can leave some students behind while failing to challenge others - leaving some of them feeling like they have failed, resulting in a lack of student engagement and motivation. 
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Our Solution:
An AI teaching assistant that leverages the power of personalization, active learning, and cognitive load management to create a more engaging and effective learning environment. Aligning our product design with these pillars in mind was intentional - they are foundational pedagogical approaches to effective learning, backed by research, and particularly well-suited to be enhanced by AI. 
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Here, we will focus on three key pedagogical concepts:
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1. Personalized Learning: Tailoring instruction to meet individual student needs, preferences, and goals.
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2. Active Learning & Retrieval Practice: Engaging students in activities that require them to actively process and recall information with an element of gamification, appealing to a wider audience within the student body. A huge trend across industries - and the ed tech world - aligns with student expectations of active learning, not only within the classroom but outside of it. 
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3. Cognitive Load Management: Optimizing the mental effort required for learning by minimizing distractions and streamlining information access.

Let's look at them one by one:

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1. Personalized Learning: Every Student's Unique Path

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What it is: Personalized learning recognizes that each student has a unique combination of strengths, weaknesses, learning styles, and goals. Personalized learning moves away from a standardized approach to education and instead focuses on tailoring the learning experience to meet individual needs. 

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This can involve adjusting the pace of instruction, the content type presented, and assessment methods (Bray & McClaskey, 2015). The concept draws heavily on constructivist learning theory, which posits that learners actively construct their own knowledge and understanding based on their experiences and interactions (Piaget, 1954).
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The LearnWise Approach to Personalized Learning:

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• User Recognition: The platform provides personalized support by identifying critical user data and tailoring responses based on the learner's history and preferences. This creates a more intuitive and relevant learning experience

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• Personalized Learning Path: Students are often made aware of their pitfalls through feedback from instructors, peer review as well as formative and summative assessments, yet they are rarely equipped to take action. Our AI assistant allows students to create flashcards, review and be quizzed on specific content they find challenging, or delve deeper in their topics of interest. 

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• Personalized Assistance: The ability to create a customized study plan for a specific course enhances time management, providing students with a realistic roadmap while reducing the burden on instructors to address each student's individual needs. LearnWise acts as a virtual tutor, supporting personalized learning journeys.
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Impact: By providing a customized learning experience, LearnWise helps students feel seen, supported, and empowered. This leads to an increase motivation, engagement, and ultimately, better learning outcomes for students.

2. Active Learning & Retrieval Practice: Engaging the Mind for Deeper Understanding

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What it is: Active learning encompasses any instructional method that engages students in the learning process, requiring them to do meaningful learning activities and think about what they are doing (Prince, 2004). A key component of active learning is retrieval practice, also known as active recall. This involves actively retrieving information from memory, which has been shown to significantly improve long-term retention and comprehension compared to passive study methods like re-reading (Karpicke & Roediger, 2008).
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The LearnWise Approach to Active Learning:
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• Study Plans and Practice Quizzes: LearnWise generates personalized study plans and practice quizzes that encourage students to actively engage with the material and retrieve information from memory. These quizzes are not just for assessment; they are designed as learning tools to help students identify knowledge gaps and reinforce understanding. The tool is also able to develop flashcards, helping student exercise long-term retention and comprehension.

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• Gamification: LearnWise encourages students to play with course content materials, by tailoring the type of questions for quizzing, shuffling their order, or having to answer correctly before moving on to the next question, making the study process less cumbersome and repetitive for students.

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• Self-Quiz Generation: Students can use LearnWise to generate personalized quizzes from course materials, further promoting active recall and retrieval practice. This empowers students to take a more active role in their learning and assess their understanding of the material.

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• Instant Course-Specific Inquiries: LearnWise provides immediate answers about grades, timelines, agendas, and more, allowing students to quickly clarify any doubts and actively engage with course content.

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• 24/7 Availability: Students can ask questions and receive explanations at anytime, encouraging them to actively seek clarification and engage with the material whenever they encounter challenges.
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Impact: By promoting active learning and retrieval practice, LearnWise helps students move beyond surface-level understanding and develop a deeper, more durable grasp of the subject matter.

3. Cognitive Load Management: Clearing the Path for Learning

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What it is: Cognitive Load Theory (CLT) is concerned with the limitations of working memory. It suggests our working memory has a limited capacity, and effective instruction should be designed to minimize extraneous cognitive load (mental effort unrelated to learning) and optimize germane cognitive load (mental effort directly related to learning) (Sweller, 1988). Extraneous load can be caused by factors such as confusing instructions, poorly designed materials, or having to search for information across multiple sources.
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The LearnWise Approach to Cognitive Load Management:
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• Unified Information Hub: LearnWise centralizes information from various sources, including the LMS, third-party apps, and external resources. This reduces the cognitive load associated with searching for information across multiple platforms, allowing students to focus their mental energy on learning.

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• Dynamic Content Updates: The platform adapts responses based on content changes, ensuring students always have the most up-to-date information. This eliminates the need to spend time and energy determining what content is current, further reducing cognitive load.

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• Streamlined Course Navigation Assistance: LearnWise helps students navigate their course structure and understand the syllabus, minimizing confusion and allowing them to focus on the content itself.

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• One Place, One Solution: student's don't have to leave the platform even when they can't find the right answers. LearnWise is able to ask context-based suggested questions, follow up accordingly, and escalate students to the right department, massively reducing cognitive load.
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Impact: By streamlining access to information and reducing cognitive load, LearnWise frees up students' mental resources for deeper learning and engagement with course content.

A Holistic Approach: Connecting Course Support with Student Services

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We recognize that student success is not solely determined by academic factors. Students often need support in other areas of life, such as mental health, financial aid, and career planning. That's why LearnWise for Course Support seamlessly integrates with LearnWise for Student Services.

How it Works: If a student asks a course-related AI assistant about mental well-being, office hours, or other non-academic topics, LearnWise can identify the query's nature and guide the student to the appropriate resources in the institution's student services platform. This ensures holistic support, addressing both academic and personal needs.

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Impact: This integrated approach creates a more supportive and cohesive learning environment. Students can access the help they need, when they need it, without having to navigate complex systems or search for information across disparate platforms. This reduces stress, improves student well-being, and ultimately contributes to higher retention and graduation rates.

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The LearnWise Difference

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We didn't just build an AI chatbot; we created a comprehensive learning companion informed by decades of research in education and cognitive science. LearnWise is designed to be more than just a tool – it's a partner in the learning process, empowering both students and educators to achieve their full potential. As we continue to develop and refine LearnWise, we remain committed to grounding our technology in sound pedagogical principles, ensuring that our AI assistant truly enhances the art and science of teaching and learning.

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Ready to experience the future of education? Book a demo today and discover how LearnWise can transform your institution.

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Works Cited
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Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7-74.
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Bray, B., & McClaskey, K. (2015). Make learning personal: The what, who, wow, where, and why. Corwin Press.
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Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966-968.
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Piaget, J. (1954). The construction of reality in the child. Basic Books.
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Prince, M. (2004). Does active learning work? A review of the research. Journal of engineering education, 93(3), 223-231.
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Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Association for Supervision and Curriculum Development.
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Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
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Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard university press.

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