Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Sidhant Bendre, Co-founder of Oleve.
Artificial intelligence is rarely a plug-and-play solution, and nowhere is this more evident than in education. Unlike industries focused on cost efficiency or convenience, education revolves around deeply human-centered objectives such as improving student outcomes and advancing societal progress. To build a sustainable future with AI, institutions are being asked to make strategic, foundational investments that address the many nuanced needs of educators and students.
This is an extraordinary moment for EdTech leaders to rethink how we teach and provide access to knowledge. By viewing AI as a platform for building scalable, equitable and sustainable learning, we can extend the reach of education—both geographically and philosophically—into new frontiers.
In this article, I’ll explore the steps I’ve learned to achieve this and some of the challenges we must address along the way.
The Right Focus: Identifying High-Impact Applications
AI can address some of education’s most persistent challenges, but its effectiveness is often diluted by broad, unfocused experimentation. Today, many schools and institutions adopt AI tools without clearly defining their goals, leading to wasted effort and skepticism about the technology’s value. For AI to make a meaningful difference, educators and technologists must focus on high-impact applications aligned with institutional priorities and systemic needs.
AI’s most effective use cases tend to fall into three broad categories:
• Streamlining operations, such as automating administrative tasks like attendance tracking, scheduling and resource allocation. While this is a practical application with immediate benefits, it only addresses surface-level inefficiencies.
• Enhancing instructional tools, including improving assessment analytics, designing targeted professional development and supporting curriculum design. This area represents a middle ground where many institutions are currently experimenting.
• Personalizing student engagement, from adaptive learning pathways to academic recommendations. This is where AI’s ability to address individual needs is most visible, but raises concerns about over-reliance on algorithms, which could disrupt classroom dynamics or sideline teacher expertise.
Laying The Groundwork For Sustainable AI Integration
Sustainability in AI begins with infrastructure. Many schools still rely on legacy infrastructure that cannot support the computational demands required for AI, so they start at a technological disadvantage. Without scalable, interoperable platforms, even the most promising AI tools risk becoming obsolete.
Key elements of a sustainable AI ecosystem include:
• Interoperability: AI systems must integrate smoothly with existing student information systems and learning management platforms. A lack of reliable data exchange can leave AI tools siloed, limiting their utility. Strong data pipelines are essential for consolidating and processing fragmented academic datasets.
• Transparency: Educators must understand how AI systems generate insights, especially when those insights influence student outcomes. Explainable AI models foster trust by allowing teachers to see the rationale behind recommendations, such as why a particular intervention is suggested for a student. This transparency ensures that teachers remain central to decision making.
• Governance: Much like AI regulation on a global and industrial scale, institutions must establish clear standards for evaluating AI tools based on instructional, ethical and equity benchmarks. These standards not only provide accountability, but they also create a framework for continuous improvement. Governance ensures that AI adoption is deliberate and aligned with educational goals, rather than reactive or haphazard.
Reimagining Educational Models
While AI can enhance existing education systems, the greatest potential comes from its ability to enable entirely new approaches to teaching and learning. Retrofitting AI into traditional models risks perpetuating inefficiencies that educators are already grappling with. Instead, technology leaders and educators should explore how AI can help redefine both pedagogy and the classroom.
One example is competency-based education. In this model, students progress based on mastery rather than time spent in class. An AI-driven competency engine can analyze performance data to determine when a student is ready to advance or needs additional support. This shifts the teacher’s role toward mentorship and personalized engagement, areas where human expertise is most valuable.
However, these advancements require corresponding updates to policies and metrics. Even the most basic things like assessment standards, funding models and credentialing systems must evolve to align with AI-enabled learning pathways. Much like governance, without these systemic changes, even the best AI use cases will struggle to achieve meaningful outcomes.
Addressing Risks And Earning Trust
The adoption of AI in education brings risks that must be carefully managed to earn the trust of educators, students and families.
Data privacy is often cited as a major concern, as educational systems process sensitive information about students and teachers. Institutions must adopt robust encryption protocols, anonymization techniques and clear data governance policies to protect this information.
Algorithmic bias is another key issue. AI systems trained on unrepresentative datasets can unintentionally reinforce inequities, particularly when scaled across diverse student populations. Institutions must adopt continuous monitoring and auditing to identify and correct biases. For example, automated audits can flag disparities in resource allocation or recommendations, ensuring fairness across student demographics.
Building trust also requires keeping educators in control. AI should empower teachers by providing actionable insights and recommendations instead of outright replacing their judgment. Systems that identify struggling students or suggest curriculum changes must leave final decisions to teachers. By presenting actionable insights instead of prescriptive directives, AI becomes a valuable collaborator in the classroom.
What’s Next For AI And EdTech?
For leaders in EdTech, we have a clear next step: AI must be made to be more than a tool for optimization. It should serve as a platform for rethinking how education is delivered and experienced. By identifying high-impact applications, modernizing infrastructure and aligning policies with innovation, we can build an education system that is more inclusive, equitable and effective.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
