Choosing Wisely: Lessons for Leaders in AI Integration

The path to their common goal is equally simple: continuous research, customer feedback, and growth.

And they’ve grown: Since we spoke seven years ago when Malkin became CEO, Carnegie has added 500 new employees, 500 adjunct faculty, four new adaptive AI products, myriad research projects, and a new Canadian headquarters.

After seven years of sitting in classrooms and talking to school leaders, a pandemic and a leap into national AI mania, Malkin today shares how schools can choose AI tools wisely and how Carnegie fits into the AI ​​landscape. We’re sharing our own insights on what to do. How AI can be a source of positive reinforcement for learners.

EdSurge: What makes Carnegie Learning’s approach to AI different from other edtech products?

Malkin: Our Origin Story was one of the first AI-driven products launched by Carnegie Mellon University through Carnegie Learning. Twenty-five years ago, Carnegie Mellon University created MATHia, the first adaptive AI-powered tutor to teach middle school math. This is still one of our core products.

Carnegie Learning was way ahead of its time with early versions of AI. Of course, the technology wasn’t as advanced as it is today, but it was still an artificial intelligence-driven, adaptive learning-driven product. When the generative AI revolution occurred, we were in a good position to step up as leaders and take that AI knowledge and apply it in ways that directly supported students and teachers.

We have people who understand the technology behind AI. We have researchers who have been studying it for 25 years. AI is already part of Carnegie Learning’s DNA, and now we are moving quickly and furiously to integrate it into our products in new and effective ways.

Based on this deep experience, what do you think school leaders and teachers can look for when choosing effective AI tools?

Many technology solutions already exist in education, but only a select few can demonstrate real value, and AI is no exception. Anyone can build technology products around education, but not everyone can create technology products influenced by learning science and research that truly make a difference.

Everyone has access to large-scale language models (LLMs) that can be used by many companies in this space to build educational applications that look the same on the surface. Only companies that understand cognitive models and learning science and have the data to add something substantive to LLM will be able to deliver meaningfully differentiated products.

For example, Carnegie Learning has been thoughtful and purposeful about integrating generative AI into their products as an enhancement to the work they’re already doing, and that’s really important.

We are integrating this into our curriculum. This is more than just a tool for educators and students to access. It’s a tool built into the Carnegie Learning Ecosystem, giving educators and students another tool in their arsenal to solve challenges and inspire them to learn more.

Schools need to consider these points when implementing AI tools. What is the actual value of the tool to students and teachers? Is it thoughtful, purposeful, and most importantly, research-based?

Research is clearly important at Carnegie. What would actually happen?

We have a large research team and are constantly Study the effectiveness We are committed to a continuous improvement system to constantly improve our products.

Their spirit is one of constant questioning and exploration.

Our research team is constantly challenging ourselves: Let’s see if there’s bias in this math problem, test it with different communities, and find out what language resonates positively to improve outcomes. How can we better understand students’ misunderstandings? Are there patterns in the types of mistakes students make? This is important.

They are never satisfied with the status quo and are very passionate about introducing product and content enhancements that make a difference.

Carnegie research teams are also working on large-scale studies, including: Gold Standard RAND Study Funding from the U.S. Department of Education and small-scale studies with school districts and schools are being conducted. Data collection and independent verification, and I emphasize that our own independent evaluations are essential to the reliability of our products. All of this influences the processes that make our products more effective. That’s a key part of our goal.

How do you think AI will support the goal of providing access to equitable and personalized learning for all students?

If we use this technology in the right way, we can give students what they want (and I remember wanting when I was a student): more engagement, more empowerment, more context. . Today’s students deserve it.

It’s great to help students understand the theory, but they also need to understand the practical application of their learning. “What can we do with this knowledge? How can we take it beyond the classroom?”

We spend a lot of time sitting in classrooms and observing. There’s no reason why curriculum and technology can’t inspire every student. Technology like adaptive AI can help significantly through personalization. And the more we can do that, the more successful we’ll be in igniting the passion within every learner.

I was struggling as a middle school math student. I remember sitting in a classroom as the lesson progressed, wrestling with the concepts that were keeping me from moving further and faster. It’s a tough place and it feels terrible. I am motivated to support all students, but especially those who require additional support. Carnegie Learning’s products, especially AI-powered solutions, are well-positioned to help students consistently reach grade level.

If we can help our students achieve even greater things, we have accomplished something great. That’s exactly what we’re doing here.