Skip to content

The Role of Artificial Intelligence in Education: How Educators Can Succeed

Empty modern classroom with digital connections transposed in the air representing AI and connectivity in the classroom.

In the modern classroom, artificial intelligence is often framed as a set of tools — a helpful assistant for grading assignments or generating lesson plans. But this practical framing only scratches the surface; there’s a much larger shift underway that’s challenging us to fundamentally rethink education.

As AI systems become more sophisticated, they raise big questions about what education should look like in the 21st century. If machines can deliver thorough content, answer complex questions, and even adapt to different learning styles, what does that mean for the role of a teacher? For the value of human judgment? For the skills we prioritize in learners?

AI forces us to reconsider long-held assumptions about how learning happens and who it serves. It exposes the limitations of one-size-fits-all models and points toward more personalized and responsive ecosystems. As the technology evolves, our educational systems must evolve alongside it — not only in how we integrate AI into education, but in how we reimagine the goals and methods of education itself.

Through the University of San Diego’s Division of Professional and Continuing Education, educators can choose from multiple courses that zero in on specific aspects of AI in education, from beginner-level classroom applications to AI’s potential impact on policy and leadership roles. Courses like the following are only the beginning of what’s possible:

Through course offerings like those above, now is the time to reexamine AI’s growing influence on the future of education: how it’s reshaping what we teach, how we teach it, and what kind of support learners need to thrive.

Redefining Education in the Age of AI

For much of modern history, formal education has centered on mastering content. Students were expected to memorize facts, follow established procedures, and arrive at correct answers. Success was measured by how well they absorbed and reproduced what they were taught.

Artificial intelligence now performs many of those same tasks — and often more efficiently than a human can. It can explain concepts and synthesize information at remarkable speed. This shift raises a profound question: If machines can deliver knowledge on demand, what should human learners focus on?

The answer lies not in outpacing AI, but in developing the kinds of thinking and communication that AI can’t easily replicate. Education must now emphasize learners’ ability to question assumptions, interpret information in context, and bring fresh ideas to unfamiliar problems. Learners need more than technical skills — they need to learn the skills of:

  • Critical thinking
  • Discernment
  • Creativity and imagination
  • Original problem-solving in unfamiliar contexts
  • Emotional intelligence
  • Collaboration
  • Ability to build relationships
  • Empathy
  • Ethical reasoning
  • Capacity to make values-based decisions

These capabilities are essential both for individual success and for navigating a future where AI will impact nearly every field. Rather than excluding AI from learning, teachers must learn to integrate it wisely, and students must learn to use it as a tool that enhances their thinking, not one that replaces it. This is a chance to reorient education toward cultivating human strengths that matter most, especially when technology can handle the rest.

The Evolving Role of Educators

For generations, teachers have served as the primary source of knowledge in the classroom. They’ve delivered lectures, assigned readings, and assessed how well students retained the material. But with AI now able to provide instant answers and detailed explanations, that traditional role is rapidly changing.

Instead of focusing solely on delivering content, teachers should work on designing environments that encourage inquiry, foster reflection, and support deeper understanding. This work involves selecting the right tools and creating the conditions for students to learn with purpose.

Rather than competing with AI, teachers can work with this technology by:

  • Guiding students to use AI tools with critical awareness and ethical responsibility
  • Helping learners interpret and apply AI-generated content within real-world and cultural contexts
  • Facilitating discussion, collaboration, and personal reflection — the kinds of interactions that require emotional presence and perspective

These human contributions can’t be replicated by algorithms, and they’re increasingly essential in a world where information is fast and cheap, but wisdom is harder to come by.

To lead in this evolving landscape, educators must develop new competencies, from designing tech-enabled pedagogy to interpreting data and navigating new digital tools. But even more important are the relational skills that bring learning to life: listening, coaching, collaborating, and inspiring.

As AI reshapes the mechanics of instruction, the need for meaningful human connection in education becomes even more urgent. Institutions will play a key role in supporting this shift by investing in professional development, redefining teaching roles, and giving educators the tools and time to adapt with confidence.

5 REASONS WHY CONTINUING EDUCATION MATTERS FOR EDUCATORS

The education industry is always changing and evolving, perhaps now more than ever. Learn how you can be prepared by downloading our eBook.

How AI Is Reshaping Learning Institutions

In addition to transforming what happens in the classrooms, AI is also changing how learning institutions operate, make decisions, and define student success.

Many schools, colleges, and universities are already using AI to streamline essential functions. Intelligent systems are being deployed to:

  • Automate administrative tasks such as admissions processing, scheduling, and academic advising
  • Track student engagement across digital platforms to identify patterns in student participation and performance
  • Flag risk factors early, enabling proactive outreach to students who may need support
  • Suggest personalized interventions based on real-time data
  • Enhance operational efficiency across departments through predictive modeling and workload optimization

But the impact of AI goes well beyond logistics. Since AI enables personalized learning at scale, institutions can move beyond fixed timelines and standardized content delivery. Education systems may even begin to shift away from rigid grade levels and seat-time requirements, opening the door to more responsive, learner-centered pathways that accommodate individual progress and needs.

This shift is also changing how learning is documented and recognized. As educational experiences become more fluid, microcredentials, skills-based certificates, and other nontraditional forms of recognition are gaining traction. AI is helping build the infrastructure to support these alternatives, tracking learning across multiple environments and validating competencies in real time.

Yet with these opportunities come complex challenges. Institutions must confront questions about how AI systems are built and governed. Issues of transparency, data privacy, and algorithmic bias demand urgent attention. Poorly designed or poorly understood AI tools can deepen inequities or make decisions that are difficult to trace or correct.

What’s clear is that institutional leadership must stay agile. The pace of AI development leaves little room for static systems or one-time solutions. Building a culture of ongoing learning and responsible experimentation will be essential for educational organizations that want to remain relevant in an AI-powered future.

The Ethics of an AI-Powered Education

As AI becomes more embedded in classrooms and campuses, so do the ethical questions that come with it. Unfortunately, these systems are not neutral; they reflect the choices, assumptions, experiences, and values of the people who design and train them. That reality raises important concerns about fairness, transparency, and accountability — especially in environments meant to support human growth and opportunity.

One pressing dilemma is bias in algorithmic decision-making. When AI systems influence how students are assessed or placed into learning tracks, subtle forms of discrimination can become systemic. Marginalized students may find themselves disadvantaged by models that were never trained to recognize or respond to their contexts. These risks are already showing up in how predictive analytics and automated assessments are used.

Data privacy is another major concern. AI-powered tools often rely on continuous streams of data to function effectively. But who owns that data? Who decides how it’s stored, shared, or monetized? Learners deserve clarity — and rights — when it comes to how their personal information is being handled.

Meanwhile, the rise of surveillance technologies is testing the boundaries of trust. AI tools can monitor everything from eye movement to typing cadence, creating a level of oversight that may feel more punitive than protective. When used without clear purpose or consent, these systems can erode student autonomy and discourage genuine engagement.

Even the most promising applications of AI are limited by access. Schools and students with more resources are better positioned to benefit from personalized tools and responsive learning platforms. Without intervention, these gaps could deepen existing inequalities and leave some communities further behind.

Navigating these challenges calls for more than technical expertise. Educators, curriculum developers, and policy leaders must be empowered to interrogate the purpose behind each AI deployment. They need to ask: Does this tool align with our values? Are we using it to enhance learning, or simply to make systems more efficient?

Ethical leadership will shape whether AI becomes a force for empowerment. It’s not enough to adopt new technologies; schools must also adopt the responsibility that comes with them.

Pros and Cons of AI in Education

As AI continues to influence educational systems, it brings both promise and risk. Understanding these tradeoffs is critical for making informed, responsible choices about how and when to integrate AI tools into learning environments.

Pros

  • Personalized learning
    AI enables instructional content and pacing to adapt to each student’s needs. This can help learners progress at their own speed or explore areas of interest more deeply, especially in environments where individual attention is hard to provide at scale.
  • Efficiency gains
    By automating repetitive administrative tasks like scheduling, AI frees up time for educators to focus on more meaningful work: mentoring students, developing curriculum, and engaging in professional growth, among other tasks.
  • Improved access
    AI-powered translation and speech-to-text tools can make learning much more inclusive for English Language Learners or students with learning difficulties or disabilities. These technologies also offer support for students with limited access to in-person instruction.
  • Data-driven insight
    Intelligent systems can analyze patterns in student engagement and performance, helping educators and institutions identify learners who need help and spot systemic issues, which leads to more informed decisions about program design and resource allocation.
  • Support for lifelong learning
    AI platforms can recommend content and track progress across formal and informal learning contexts. This opens up new possibilities for continuing education and upskilling throughout a person’s life — not just during their grade school years.

Cons

  • Equity gaps
    Access to AI tools often depends on a school or district’s financial resources. Without deliberate intervention, this technology can widen existing inequalities between well-funded and under-resourced communities.
  • Privacy and surveillance
    Many AI systems rely on continuous data collection to function. Without strong safeguards, this can lead to invasive monitoring that compromises student autonomy, erodes trust, and exposes personal information to misuse.
  • Algorithmic bias
    AI models trained on incomplete or biased data can reinforce harmful stereotypes or make flawed predictions. These biases can affect everything from admissions decisions to automated feedback, often in ways that are difficult to detect or correct.
  • Overreliance
    When AI tools are treated as infallible or used to replace human judgment, students may lose opportunities to think critically or develop essential skills. Educators may feel pressure to defer to systems instead of using their professional expertise or discretion.
  • Dehumanization of learning
    At its worst, AI can reduce education to a series of transactions — inputs and outputs optimized for efficiency. This risks stripping away the relational, emotional, and ethical dimensions of learning that help students become thoughtful, engaged citizens.

Ultimately, the value of AI in education will depend not just on what it can do, but on how intentionally and ethically it’s applied.

What Educators and Leaders Can Do

As AI becomes increasingly embedded in educational systems, educators, institutions, policymakers, and developers have a responsibility to ensure that it strengthens, rather than undermines, the human core of learning.

For Educators

Teachers play a crucial role in helping students navigate an AI-powered world. That begins with deepening their own understanding. Teachers can:

  • Seek professional learning opportunities that go beyond tool training to include ethical evaluation, instructional fit, and long-term impact.
  • Cultivate AI literacy to recognize what these systems can and can’t do and where human insight remains essential.
  • Help students think critically about AI-generated outputs, guiding them to ask sharper questions and develop stronger reasoning.
  • Embrace evolving responsibilities as mentors, facilitators, and ethical guides in the classroom.
  • Advocate for equitable access to AI tools so that all students can benefit, regardless of background or school funding.

For Institutions

The choices institutions make now will shape the educational landscape for years to come. Responsible integration of AI calls for coordinated action and sustained investment. Institutional leadership can:

  • Provide educators with continuous training and planning time to use AI thoughtfully and effectively.
  • Embed AI ethics and digital literacy into the curriculum, not just in computer science classes but across subjects and grade levels.
  • Involve faculty and students in evaluating and selecting AI tools to ensure they meet real instructional needs.
  • Design infrastructure and support systems that promote equitable adoption and close resource gaps between communities.
  • Create transparent, student-centered data policies that clearly define how data is collected, stored, and used.

For Policymakers

Public trust and safety must be foundational to AI adoption in education. That requires governance that is proactive, inclusive, and enforceable. Educational policymakers can:

  • Establish regulatory frameworks to address key concerns like privacy, surveillance, and bias in educational AI systems.
  • Promote transparency and accountability in AI tools used for high-stakes functions such as admissions and testing.
  • Pursue education and learning opportunities to understand the possibilities and limitations of AI in education
  • Invest in equitable implementation, especially in underfunded schools, through targeted funding and incentive programs.
  • Encourage research and public dialogue that examines AI’s long-term impact on learning, labor, and equity.

For EdTech Developers and Industry Partners

Technology designed in isolation from the classroom rarely meets the complex needs of real learners. Developers must collaborate with the communities they serve; they can:

  • Partner with educators and students to design tools that address real instructional challenges — not just efficiency metrics.
  • Prioritize explainability so users can understand, question, and adjust AI recommendations.
  • Build inclusive datasets and audit tools for bias to prevent reinforcement of existing disparities.
  • Be transparent about how AI tools are trained, what data they use, and where their limitations lie.

We’re in a defining moment for education, where the systems we build today will influence generations of learners. To engage with AI constructively, educators and leaders must commit to continual learning and collaboration.

Courses such as Introduction to Artificial Intelligence and Education from USD’s Division of Professional and Continuing Education offer a timely and thoughtful starting point. Grounded in both practical application and ethical insight, this course helps educators build the confidence and clarity to lead in a time of rapid change.

The future of education is not something to wait for. It’s something we’re shaping right now.

Advance

Your Salary

Browse over 500+ educator courses and numerous certificates to enhance your curriculum and earn credit toward salary advancement.