AI in Academics: Building a Sustainable Future for Education

Artificial intelligence (AI) is transforming industries across the globe, but its application in education is uniquely complex, according to Forbes. Unlike sectors driven by cost efficiency or convenience, education is deeply rooted in human-centered goals—improving student outcomes, fostering equity, and advancing societal progress. As Sidhant Bendre, Co-founder of Oleve and Forbes Technology Council member, highlights in his recent article, building a sustainable future with AI in education requires strategic investments, thoughtful integration, and a focus on high-impact applications.

AI has the potential to address some of education’s most persistent challenges, but its effectiveness depends on how it’s implemented. Bendre emphasizes that many institutions adopt AI tools without clearly defined goals, leading to wasted effort and skepticism. To make a meaningful difference, educators and technologists must focus on high-impact applications that align with institutional priorities.

Bendre identifies three key categories where AI can drive significant change:

1. Streamlining Operations: Automating administrative tasks like attendance tracking, scheduling, and resource allocation can save time and reduce inefficiencies. However, these applications only scratch the surface of AI’s potential.
2. Enhancing Instructional Tools: AI can improve assessment analytics, support curriculum design, and provide targeted professional development for educators. This middle ground is where many institutions are currently experimenting.
3. Personalizing Student Engagement: Adaptive learning pathways and academic recommendations can address individual student needs. While this is one of AI’s most visible benefits, it also raises concerns about over-reliance on algorithms and the potential sidelining of teacher expertise.

For AI to be sustainable in education, institutions must invest in the right infrastructure. Many schools still rely on legacy systems that cannot support the computational demands of AI, putting them at a technological disadvantage. Bendre outlines three critical elements for building a sustainable AI ecosystem:

1. Interoperability: AI systems must integrate seamlessly with existing student information systems and learning management platforms. Strong data pipelines are essential for consolidating fragmented academic datasets.
2. Transparency: Educators need to 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.
3. Governance: Institutions must establish clear standards for evaluating AI tools based on instructional, ethical, and equity benchmarks. This ensures accountability and aligns AI adoption with educational goals.

While AI can enhance existing systems, its greatest potential lies in enabling entirely new approaches to teaching and learning. Bendre suggests that retrofitting AI into traditional models risks perpetuating inefficiencies. Instead, educators and technologists should explore how AI can redefine pedagogy and the classroom.

One promising example is competency-based education, where 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 systemic changes. Assessment standards, funding models, and credentialing systems must evolve to align with AI-enabled learning pathways. Without these updates, even the best AI use cases will struggle to achieve meaningful outcomes.

The adoption of AI in education comes with risks that must be carefully managed to earn the trust of educators, students, and families.

• Data Privacy: Educational systems process sensitive information, making robust encryption protocols, anonymization techniques, and clear data governance policies essential.
• Algorithmic Bias: AI systems trained on unrepresentative datasets can unintentionally reinforce inequities. Continuous monitoring and auditing are necessary to identify and correct biases.
• Teacher Autonomy: AI should empower teachers by providing actionable insights rather than replacing their judgment. Systems that suggest interventions or curriculum changes must leave final decisions to educators.

For EdTech leaders, the next step is clear: AI must be more than a tool for optimization. It should serve as a platform for rethinking how education is delivered and experienced. By focusing on high-impact applications, modernizing infrastructure, and aligning policies with innovation, we can build an education system that is more inclusive, equitable, and effective.

As Bendre concludes, “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.”