{"id":621,"date":"2026-10-01T21:17:02","date_gmt":"2026-10-02T01:17:02","guid":{"rendered":"https:\/\/solutionsreview.com\/edtech\/?p=621"},"modified":"2026-10-01T21:26:38","modified_gmt":"2026-10-02T01:26:38","slug":"the-future-of-education-with-ai-what-should-schools-become","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/edtech\/2026\/10\/01\/the-future-of-education-with-ai-what-should-schools-become\/","title":{"rendered":"The Future of Education With AI: What Should Schools Become?"},"content":{"rendered":"

Solutions Review’s Tim King explores the future of education with AI and why schools may need to focus more on human skills, judgment, and mentorship.<\/strong><\/em><\/p>\n

The future of education with AI<\/strong> is often discussed as a technology question.<\/p>\n

How will students use AI? Which tools should schools adopt? Will AI tutors personalize learning? How should educators prevent cheating? What tasks can teachers automate?<\/p>\n

But those questions may be downstream from a much larger one.<\/p>\n

If intelligence, tutoring, content creation, and access to information become increasingly abundant, what should schools and universities actually be for?<\/p>\n

That question anchored the “What Should Education Become?<\/a><\/span>“<\/strong> panel during the recent Q3 2026 Mini Jam on Insight Jam<\/em><\/strong><\/a><\/span>, where education leaders and innovators examined what teaching and learning should look like in an age of abundant intelligence.<\/p>\n

Rather than simply debating how AI should fit into the existing education system, the conversation explored how the system itself may need to change.<\/p>\n

As AI becomes more capable, education may have to become much clearer about something it has historically struggled to define: what students still uniquely need schools, universities, and educators to provide.<\/p>\n

The Future of Education With AI Starts With a Different Question<\/h2>\n

Education technology tends to create an understandable pattern.<\/p>\n

A new technology arrives. Schools determine whether to allow it. Educators experiment with it. Institutions develop policies around it. Eventually, the technology becomes incorporated into existing educational processes.<\/p>\n

Generative AI is moving so quickly that this approach may no longer be sufficient.<\/p>\n

The opening question of the Insight Jam panel captured the problem: If intelligence, tutoring, and content delivery become abundant and effectively free, what is the core job of a school or university?<\/p>\n

And perhaps more importantly: What are educational institutions preparing people to become?<\/strong><\/p>\n

Those questions shift the conversation from technology adoption toward institutional purpose.<\/p>\n

Schools have traditionally served many functions simultaneously. They deliver information, provide instruction, assess knowledge, develop skills, award credentials, create communities, expose students to new ideas, prepare people for careers, and connect learners with educators and peers.<\/p>\n

AI could change the relative value of each of those functions.<\/p>\n

If information becomes easier to access, delivering information becomes less differentiating.<\/p>\n

If personalized tutoring becomes widely available, access to explanations becomes less scarce.<\/p>\n

If AI can generate increasingly sophisticated work, producing a finished artifact becomes less meaningful as standalone evidence of learning.<\/p>\n

But other parts of education may become more valuable precisely because technology cannot easily replace them.<\/p>\n

Pedagogy Should Come Before AI Technology<\/h2>\n

One of the clearest principles to emerge from the panel was also one of the simplest:<\/p>\n

Start with how people learn, not with the technology.<\/strong><\/p>\n

AI doesn’t eliminate the need for learning science, pedagogy, or instructional design.<\/p>\n

Instead of beginning with a new AI tool and searching for ways to incorporate it into the classroom, educators can begin with the learner and the outcome they are trying to create.<\/p>\n

Then they can determine whether technology helps.<\/p>\n

As one panelist argued, faculty members can stay grounded by focusing first on learning science and pedagogy and only afterward connecting those principles to the appropriate technology.<\/p>\n

That distinction becomes increasingly important as the number of AI products available to educators continues to expand.<\/p>\n

Technology is not the educational objective.<\/p>\n

Learning is.<\/p>\n

An institution can deploy sophisticated AI across every classroom without necessarily improving education. Likewise, reducing technology does not automatically improve learning.<\/p>\n

The more useful question is whether a particular tool strengthens the educational experience the institution is trying to create.<\/p>\n

Education Has to Prepare Students for Work That Doesn’t Exist Yet<\/h2>\n

The future of education with AI is also inseparable from the future of work.<\/p>\n

Students graduating today may spend their careers performing jobs that look substantially different from the roles that currently exist. Some of the tasks they are learning to perform will be automated. New responsibilities will emerge. Existing professions will incorporate AI in ways that are still difficult to predict.<\/p>\n

That makes preparing students for one narrow collection of tasks increasingly difficult.<\/p>\n

Instead, several panelists emphasized capabilities that transfer across jobs and technologies.<\/p>\n

Critical thinking.<\/p>\n

Communication.<\/p>\n

Creativity.<\/p>\n

Collaboration.<\/p>\n

Questioning.<\/p>\n

Judgment.<\/p>\n

Applied problem-solving.<\/p>\n

One panelist recalled advice from an HR executive who didn’t necessarily need schools to teach graduates exactly how to perform a particular job. Employers could teach the specifics. What mattered was receiving graduates capable of thinking about how to do the job, communicating effectively, and advocating for what they needed.<\/p>\n

That distinction becomes even more important in an AI-driven workforce.<\/p>\n

Educational institutions cannot perfectly predict which tools students will use ten years from now.<\/p>\n

They can develop people capable of adapting when those tools change.<\/p>\n

The panel discussed practical learning experiences, case studies, scenario-based education, internships, Socratic questioning, and what one participant called developing “unstructured problem solvers.”<\/p>\n

The objective is not merely to teach students to operate AI.<\/p>\n

It is to prepare students to think when AI changes what the job requires.<\/p>\n

Students Need to Learn When Not to Use AI<\/h2>\n

AI literacy is often framed around knowing how to use artificial intelligence effectively.<\/p>\n

The future of education may require a broader definition.<\/p>\n

Students will also need to know when not to use it<\/strong>.<\/p>\n

That decision itself is a form of judgment.<\/p>\n

One panelist described the importance of students developing the fluency to determine when AI is appropriate and when human engagement, independent thought, or another approach is required.<\/p>\n

That moves AI education beyond prompting and tool proficiency.<\/p>\n

A student entering an AI-enabled workplace may need to determine when AI can accelerate routine work, when its output requires verification, when sensitive circumstances demand human judgment, and when delegating the task would eliminate an experience necessary for developing expertise.<\/p>\n

Knowing how to use AI will matter.<\/p>\n

Knowing when not<\/strong> to use it may matter just as much.<\/p>\n

Teachers Are Becoming Coaches and Mentors<\/h2>\n

If AI can increasingly explain concepts, generate instructional materials, answer questions, and personalize certain learning experiences, the educator’s role does not disappear.<\/p>\n

But parts of it may shift.<\/p>\n

The panel discussed educators becoming increasingly important as facilitators, coaches, and mentors.<\/p>\n

That puts greater emphasis on capabilities that aren’t reducible to content delivery: understanding students, building relationships, motivating learners, creating productive environments, providing context, facilitating difficult conversations, and helping students develop judgment.<\/p>\n

But there is an institutional complication.<\/p>\n

Faculty members are not automatically trained for that role.<\/p>\n

One panelist noted that institutions may need to support educators not only with AI training but also with the distinctly non-AI capabilities<\/strong> required as faculty move further toward coaching and mentorship.<\/p>\n

That means the future of education with AI requires investment in educators, not simply investment in educational technology.<\/p>\n

Institutions may need to rethink what excellent teaching looks like\u2014and then align professional development, evaluation, incentives, and support around that new definition.<\/p>\n

Assessment Should Measure More Than the Final Answer<\/h2>\n

AI also creates a problem for one of education’s most established practices: evaluating finished work.<\/p>\n

If generative AI can produce a sophisticated essay, solve a problem, create a presentation, or generate an answer, the final artifact provides less information about the process that produced it.<\/p>\n

That doesn’t necessarily mean traditional assessments disappear.<\/p>\n

It means institutions may need additional evidence.<\/p>\n

During the panel, educators discussed shifting some attention from outcomes toward the learning process itself. Rather than simply asking whether a student reached the correct destination, assessment can examine how the student got there, how technology was used, what decisions were made, and whether the learner can explain or defend the work.<\/p>\n

That shift complements a broader transformation already underway in AI education.<\/p>\n

The important capability may increasingly be less about producing an answer and more about interrogating, evaluating, improving, and applying answers<\/strong>.<\/p>\n

The Future of Education With AI May Be More Human<\/h2>\n

One of the most interesting conclusions from the panel is also somewhat counterintuitive.<\/p>\n

AI could push education to become more human.<\/p>\n

Before generative AI, schools could spend enormous amounts of time focused on information, assignments, grades, skills mastery, and content delivery without necessarily confronting a deeper question:<\/p>\n

What is distinctly human about education?<\/strong><\/p>\n

AI is forcing that conversation.<\/p>\n

Panelists repeatedly returned to human connection, relationships, belonging, compassion, and the need for students to feel seen and valued.<\/p>\n

One participant argued that AI is forcing educators to discuss what it means to be human in ways education should perhaps have been doing already. Another emphasized that students need a foundational sense of connection and belonging before they can fully engage in learning.<\/p>\n

That suggests an important paradox.<\/p>\n

The more capable educational technology becomes, the more valuable the distinctly human parts of education may become.<\/p>\n

Schools Still Have Something AI Cannot Make Abundant<\/h2>\n

Near the end of the Q3 2026 Mini Jam panel, participants were asked to identify a human capability schools should protect at all costs.<\/p>\n

Their answers were revealing.<\/p>\n

The ability to make the final call.<\/p>\n

The humanity behind an answer.<\/p>\n

Connections between educators and students.<\/p>\n

Human relationships and mental health.<\/p>\n

The ability to recognize when AI is wrong.<\/p>\n

None of those capabilities depend on humans outperforming AI at information retrieval.<\/p>\n

They depend on judgment, relationships, values, context, and responsibility.<\/p>\n

That may provide a useful framework for thinking about the future of education with AI.<\/p>\n

Schools and universities do not necessarily need to compete with artificial intelligence at what artificial intelligence does best.<\/p>\n

They need to become much better at developing what students will still need when intelligent assistance is everywhere.<\/p>\n