For Paul M. Martinez, an AI software engineering student at Maestro College, feeling heard is part of what makes his education feel personal. “When I reach out, I receive guidance and support, and I have never felt unimportant or ignored,” he says. 

His experience points to one aspect of the college’s approach to personalization: staff pay attention to students’ work and respond when they need guidance. Maestro is an online college where an AI tutor guides students through course material, adapting explanations and practice to their understanding, while faculty grade their work and give written feedback. 

Students report that the combination works for them. In the college's August 2026 Academic Quality and Student Satisfaction Survey, 93 percent of 3,703 respondents agreed that the curriculum, instruction and AI learning tools met their expectations. 

For higher education leaders, the model raises a practical question: how can AI-supported study and faculty judgment work together to help students advance? 

How does AI personalize learning at Maestro College? 

Students encounter difficulties at different points in a course. While one student might need further guidance before attempting an exercise, another understands a concept but struggles to apply it. A third student wants to explore a question before moving on. 

At Maestro, AI supports an ongoing conversation around course material. Students can ask questions, revisit an explanation and connect concepts to familiar situations. The tutor also carries context across lessons, including concepts students have demonstrated they understand, areas where they continue to struggle, and teaching approaches that work best for them. 

Students rate each lesson as they complete it. In the 12 months to October 2026, students submitted 290,231 ratings, with lessons averaging 4.85 out of five. 

Written course feedback makes that experience concrete. One student, who directs a town water and sewer department, writes that having lessons tailored to their work “makes everything as a whole click so much easier”. Another student values being able to “ask questions when I’m unsure of something without feeling embarrassed or afraid of criticism”. 

Frank Huxley, an AI software engineering associate degree student from the June 2026 cohort, describes the value of receiving individual attention: “I especially appreciate having my own personal AI tutor that understands me and teaches me in the way that helps me build the skills I need to succeed.” 

For student Ingo Ngoyama, availability also matters. In written feedback, he describes being able to wake at 3 a.m., ask a question and receive an immediate response. 

Flexibility extends to both how students learn and when they study. In the August 2026 survey, 87 percent of respondents agree that they could not pursue their degree if all coursework had to be completed on campus. For these respondents, online access was a condition of participation. 

The research behind personalized learning 

Designing courses for online students involves more than making materials available remotely. 

In a study of an online psychology course, Koedinger et al. (2015) found that completing interactive learning activities was more strongly associated with learning outcomes than watching videos or reading text. 

More recent research examined how AI supports active learning. In a randomized study of two lessons in a Harvard undergraduate physics course, Kestin et al. (2025) found greater short-term learning gains among students using a purpose-built AI tutor than among those in an in-class active-learning session. The study’s authors tied the result to the tutor’s research-based design. 

Maestro’s lessons are built on the same principle: students work through material by asking questions, practicing and checking their understanding, rather than only reading or watching. 

How faculty assess learning and provide feedback 

Ratings show how a lesson feels; they do not show what a student can do. That gap is where Maestro’s faculty work: instructors judge whether students can apply what they have studied, looking at both the work students produce and the understanding they can explain. 

In Maestro’s Web Fundamentals course, the final project is an interactive application that displays information about countries. Students build the interface, add interactive controls and connect it to real data from an external application programming interface. They then answer guided questions explaining how it works. 

Together, the application and the explanations give instructors a basis for assessing practical execution and conceptual understanding. Can the student combine skills introduced separately throughout the course? Do they understand how information moves from an external source to the screen? Can they explain the relationship between their code and the application’s behavior? 

Maestro’s faculty review guidelines ask instructors to assess students’ work against course criteria while recognizing valid solutions that take a different approach from the expected one. In their written feedback, instructors explain how the work meets those criteria and where it needs further development. 

Across the college, more than 140 faculty members graded 41,644 exams and assignments with written feedback in the 12 months to October 2026, according to the college’s internal summary.  

Course discussions show the same attention. In an introductory JavaScript course, students were asked to explain asynchronous programming to a friend. One compared it to having several browser tabs open at once. John S. Berta, who teaches software engineering at Maestro, sharpened the idea rather than simply praising it. The asynchronous part, he wrote, is the browser “continuing to accept input while content elsewhere is still being retrieved, not simply the number of tabs that are open”. 

What personalized learning looks like in practice 

A practical way to judge personalization is to ask what happens when a student struggles, who notices and what changes as a result. A question during study may reveal a difficulty worth revisiting in an assessment. An assignment may expose a misunderstanding that calls for another explanation. Faculty feedback may give the student a clearer focus for the next round of practice. 

Each source of information serves a purpose. Student feedback helps the college understand the learning experience. Participation data shows how students engage with coursework. Assessed work provides evidence of understanding and application. 

For students, those contributions come together in everyday terms: being able to study when life allows, learning through explanations and examples that make sense to them, and receiving attention for the work they have done. 

About the data: Lesson-rating and faculty-activity figures are institution-reported totals for the 12 months to October 2026, as summarized by Maestro College. Lesson ratings count ratings submitted, rather than distinct students. Survey findings refer to 3,703 completed responses collected in August 2026, about 42 percent of the students shown the survey. Agreement combines “strongly agree” and “somewhat agree”. The expectations item covered curriculum, instruction and AI learning tools together and cannot isolate the contribution of any one element. Unattributed student comments come from anonymous end-of-course surveys collected between July 12 and August 8, 2026. Ratings and survey responses describe reported experiences and perceptions. Activity counts describe faculty involvement. These measures do not establish learning gains or degree-completion outcomes. 

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