AI in Action: How AI Can Enhance Nutrition Education

From Promise to Progress: How AI is Powering a New Wave of Adaptive Learning Tools

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The Long Road of Adaptive Learning

        For years, adaptive learning has been hailed as a game-changer in education, promising personalized, data-driven instruction that meets each student where they are. However, the reality has often fallen short, with many systems failing to deliver on their potential. Recent advances in artificial intelligence (AI) may finally shift that narrative. AI-powered adaptive platforms are becoming more capable of tailoring learning experiences to individual needs, improving engagement and academic outcomes. In this post, I’ll highlight three case studies, from Indiana’s Penn Harris Madison Middle Schools, Arizona State University, and Colorado Technical University, to explore how adaptive learning is used in real classrooms. Adaptive learning is a promising technology that, when implemented effectively, can be a powerful partner in revolutionizing personalized learning.

Carnegie Learning at Penn Harris Madison Middle Schools

Combining MATHbook with MATHia, an
intelligent tutoring software.

        One powerful example of adaptive learning in action comes from the Penn Harris Madison School Corporation in Indiana. In the 2020–21 school year, the district implemented Carnegie Learning’s Middle School Math Solution across its three middle schools. This program blends hands-on learning through MATHbook—an interactive, write-in textbook—with MATHia, an intelligent tutoring software that adapts in real time to each student’s progress and needs. Teachers reported noticeable improvements in student engagement, confidence, and performance. Students with Individualized Education Plans (IEPs) particularly benefited from the personalized learning paths and targeted support. Additionally, the platform’s real-time data gave educators valuable insights that allowed for more responsive, tailored instruction. This case highlights how, when implemented effectively, adaptive learning tools can enhance not only student outcomes but also empower teachers with the information they need to better support diverse learners (Carnegie Learning, 2023).

Arizona State University's Partnership with Knewton and Cengage Learning

KNEWTON

        In 2015, Arizona State University (ASU) embarked on an ambitious initiative to enhance student success and retention by integrating adaptive learning technologies into its high-enrollment general education courses. Collaborating with Knewton and Cengage Learning, ASU developed "Active Adaptive" learning solutions, beginning with an introductory psychology course. This approach combined Knewton's adaptive technology with Cengage's MindTap platform, aiming to personalize learning experiences and allow instructors to focus more on student support rather than traditional lecturing. The adaptive system provided real-time analytics, enabling timely interventions for students at risk of falling behind. The implementation of these tools led to measurable improvements in student outcomes. For instance, in College Algebra, the success rate (students earning a grade of C or better) increased from 57% in 2015 to 85% in 2019 following the adoption of adaptive courseware. Additionally, the number of course sections utilizing adaptive technologies grew from 44 in the 2016–2017 academic year to 65 by Fall 2019, serving over 25,000 students in redesigned gateway courses during the 2018–2019 academic year (Every Learner Everywhere, 2020). This case highlights how adaptive learning, when integrated thoughtfully, can make a significant impact on both the student experience and overall academic performance in higher education.

Colorado Technical University's Adaptive Learning Implementation 

Three avatars that were part of CTU's
undergraduates course

        Since 2012, Colorado Technical University (CTU) has been a leader in implementing adaptive learning technology to better serve its primarily nontraditional student population, many of whom balance school with work, family, or military commitments. CTU’s platform, intellipath, dynamically adjusts course content to meet students where they are, offering individualized learning paths based on prior knowledge and performance. By 2020, CTU had scaled adaptive learning across a wide range of programs, training over 600 faculty members to effectively engage with students in these environments (Johnson & Sloan, 2020). The university’s adaptive courses include interactive elements like simulations, videos, avatars, and discussion boards, which increase student engagement and make content more accessible and meaningful. One of the most impactful aspects of CTU’s implementation is its integration of instructor presence, adaptive learning here does not replace faculty but instead enhances their ability to support students through targeted feedback and interventions. The combination of technology and human connection has resulted in higher persistence rates, increased student satisfaction, and improved academic performance. CTU’s case demonstrates that adaptive learning when paired with intentional design and faculty involvement, can effectively personalize the online learning experience without compromising the quality of instruction.

What These Case Studies Tell Us 

        These case studies suggest that adaptive learning is beginning to deliver on its long-standing promise when it's implemented with intention and support. From middle schools in Indiana to major universities like ASU and CTU, we’re seeing improved student outcomes, increased engagement, and better use of instructional time. However, challenges remain. These tools can be expensive to implement and maintain, and not all schools have equitable access to the necessary technology or infrastructure. There’s also the concern of algorithmic bias, which can unintentionally reinforce learning gaps if the systems aren’t carefully monitored. Teacher training is another hurdle, educators need time and support to effectively integrate adaptive platforms into their instruction. And most importantly, these case studies show that the human element is still crucial. Whether it's a middle school math teacher using real-time data from MATHia, a college professor leveraging analytics to coach students or an online instructor personalizing outreach in CTU’s intellipath system, adaptive learning is most effective when it enhances, rather than replaces, the educator’s role.

What This Means for Teaching and Learning Spaces 

        Educators looking to incorporate adaptive learning into their classrooms can start small by exploring platforms that align with their subject matter and student needs; tools like MATHia for math or MindTap for introductory college courses are great examples. For instructional designers and trainers, it’s important to evaluate how these tools can complement instructional goals, ensure content is accessible, and provide training for facilitators. A thoughtful integration plan that includes ongoing evaluation and teacher support is key. Personally, I believe adaptive learning should continue moving toward a hybrid model where technology enhances human-led instruction rather than attempting to replace it. When used as a partner, adaptive tools can provide valuable data, personalize content delivery, and free up educators to focus on higher-order tasks like mentoring, facilitating collaboration, and fostering critical thinking. The future of adaptive learning should focus on equity, ethics, and the empowerment of both learners and educators through thoughtful design and implementation.

Conclusion 

        So, is adaptive learning finally living up to the hype? Based on these case studies, the answer is, it's getting there. While past promises may have outpaced results, recent examples from K-12 to higher ed show real progress, especially when adaptive platforms are used thoughtfully and paired with strong instructional design and educator support. Still, technology alone isn’t the answer. The real power of adaptive learning lies in how it’s used to empower teachers and personalize the student experience. As we move forward, balancing innovation with a human-centered approach will be key to creating meaningful, inclusive, and effective learning environments.

Disclosures

ChatGPT was used to generate a blog post outline and for sentence clarification.

ChatGPT was used for image generation. 

References 

Every Learner Everywhere. (2020). Arizona State University: Creating a culture of adaptive learning.             https://www.everylearnereverywhere.org/wp-content/uploads/case-study-arizona-state-        
         university_FINAL.pdf

Johnson, C., & Sloan, A. (2020, April 6). Adaptive learning: Implementation, scaling, and lessons         
         learned. EDUCAUSE Review. https://er.educause.edu/articles/2020/4/adaptive-learning-            
         implementation-scaling-and-lessons-learned

Kamenetz, A. (2011, October 16). Knewton’s “Adaptive learning” technology spreads to tens of thousands of students at ASU, Penn State, SUNY, more. Fast Company. https://www.fastcompany.com/1760309/knewtons-adaptive-learning-technology-spreads-tens-thousands-students-asu-penn-state-suny-mo

Lapowsky, I. (2015, August 26). This robot tutor will make personalizing education easy. Wired. https://www.wired.com/2015/08/knewton-robot-tutor/?utm_source=chatgpt.com

Penn Harris Madison. (2022, January). Student Confidence and Leadership Soar at Penn Harris Madison Middle Schools. Carnegie Learning. https://cdn.carnegielearning.com/assets/research/Penn_Harris_Madison_Case_Study.pdf?_gl=1*tzoyku*_gcl_au*MTg5Nzk0MDM3MC4xNzQ0MzE4NjQz*_ga*MjI0NzQyODAuMTc0NDMxODY0NA..*_ga_HT75DMPVPG*MTc0NDQxMjI1NC41LjEuMTc0NDQxMjI2NS40OS4wLjA.

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         Vignare, K., Tesene, M., and Lorenzo, G. (2020, July 13) Case Study: Arizona State University                [Case Study] Association of Public & Land-grant Universities and Every Learner Everywhere.                 https:// www.everylearnereverywhere.org/resources/case-study-arizona-state-university-asu/

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