Top 15 Challenges of Artificial Intelligence in 2025

AI Is Changing How We Learn: The New Role Of Human Teachers

Students, teachers and technologies are increasingly part of the same learning equation. They can be ... [+] configured deliberately to harness NI and AI in complementarity to nurture HI.

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One evening, a high school English teacher, reads an intriguing essay from her student. Well-structured and fluent, the paper includes several Bible verses that catch her attention — yet turn out to be nonexistent. Rather than marking the paper as plagiarized, she invites the student to a conversation. With genuine curiosity, she asks him about his research process. When he reveals his growing reliance on generative AI to help him write text, she uses this as a teaching moment. Rather than shaming him, she seizes it as an opportunity to help him see the value of critical thinking, and the need for him to boost his curiosity and creativity now, as a defense against the acute risk of agency erosion amid Ai.

This incident highlights the tension between artificial and natural Intelligences: while AI excels at processing data, it lacks the deeper dimensions of human understanding that characterize NI.

Natural Intelligence: A Multidimensional Framework

Going far beyond the rational thought process the type of intelligence that each of us is naturally equipped with operates on multiple levels that AI cannot replicate, so far:

Personal Aspects:

Aspirations: Our goals and visions that animate us to learn and give knowledge purpose

Emotions: Empathy, compassion, and other feelings that shape how we interpret experiences

Thoughts: Logical reasoning, creativity, and moral judgment that converge in our thinking

Sensations: Our embodied awareness of the world that can trigger intuition or creativity

Collective Levels:

micro: The individual self with unique traits and abilities

meso: Our immediate communities—families, classrooms, workplaces

macro: Larger societal systems like education policies or media

meta: The global environment and broader natural world

Learning happens within these overlapping contexts. In our entry case the teacher recognized that addressing her student's AI use required understanding his personal pressures (micro), the competitive classroom culture (meso), broader academic expectations (macro), and even how technology is reshaping society (meta).

AI Illusions: Understanding Limitations

AI generates impressive content but often lacks contextual grounding. Advanced language models rely on pattern recognition, predicting the most likely words to follow a prompt. This leads to hallucinations where AI fabricates facts or references when data is incomplete. The other risk resides with confabulations when AI confidently presents coherent but fictional narratives

When AI inserts scholarly sources that don't exist in the real world it does not come with the intent of deception in the human sense. AI models don't understand truth or falsehood; they merely generate patterns that mimic authoritative language. What makes this particularly challenging is the polished, articulate nature of AI outputs, which can easily convince even discerning readers. This is precisely why teachers are irreplaceable. They help students develop cognitive agency — the ability to think independently despite technological shortcuts — before entering workplaces where time pressures constantly tempt them to outsource their thinking. Just as physical strength requires consistent exercise, critical thinking is a muscle that atrophies without use. Teachers serve as vital trainers, guiding students to flex their curiosity, creativity, and analytical skills in a world that increasingly rewards the passive consumption of machine-generated content.

The Changing Role Of Teachers

As AI handles more knowledge transfer, the teacher's role shifts dramatically:

From knowledge provider to:

Values Ambassador: Modeling integrity, persistence, and ethical reasoning

Emotional Guide: Creating safe spaces for students to express doubts, fears, and hopes

Critical Thinking Mentor: Teaching students to question sources, recognize biases, and verify information

Connection Builder: Fostering human relationships that give learning meaning and context

Our entry case teacher embodied this transformation. Rather than simply correcting the student's mistake, she helped him see why thinking independently matters. She shared her own struggles with information overload and built a stronger relationship through honest dialogue. Such human connection — impossible for AI to replicate — is part of the mental foundation that students walk away with when they leave school. More than the knowledge that they have absorbed, the experiences and values that they have been exposed to will shape their mindsets for the next stages of their life.

Double Literacy: Digital And Human

To navigate a world enriched yet complicated by AI, we need two types of literacy:

Digital Literacy:

Understanding how AI tools work, their strengths and limitations

Identifying biases and potential misinformation in AI outputs

Building habits of cross-referencing and fact-checking

Experimentation with tools and techniques with creativity

Human Literacy:

A holistic understanding of brain and body, self and society

Emotional intelligence with the deliberate cultivation of empathy, moral reasoning, and cultural awareness

Critical thinking

Personal meaning making with awareness of the ripple effect that individual choices have in communities, societies, and the natural world.

After their initial conversation, our teacher developed a classroom exercise where students evaluated AI-generated content alongside human-written work, strengthening both their digital and human literacy. She created discussion circles where students could share their emotional reactions to AI and their fears or hopes about technology's impact on their futures — addressing not just technical skills but deeper human concerns.

Hybrid Intelligence: The Path Forward

A teacher who excels in hybrid intelligence maximizes the benefits of AI and NI:

AI Strengths:

Rapid data processing and analysis

Handling repetitive tasks

Providing immediate feedback

Playing with alternative perspectives

Human Insights:

Addressing emotional well-being and moral judgment

Interpreting AI outputs through broader contexts

Recognizing and defending values and ethical boundaries

Sensitivity to interpersonal relationships

Rather than banning AI tools, teachers should incorporate them deliberately into their classroom. Consider an environment where students use AI for initial research but then apply their own critical thinking to evaluate outputs. A space where students are provided with emotionally intelligent guidance on when to rely on technology and when to trust human judgment, creating meaningful learning experiences that neither human nor machine could offer alone.

Four Steps To Start Building Hybrid Intelligence in Education

Awareness: Stay informed about AI tools and their potential biases. Recognize "hallucinations" and question sources rather than accepting outputs at face value.

Appreciation: Value the unique depth human intelligence brings to learning — our aspirations, emotions, thoughts, and sensations across all levels of experience.

Acceptance: Welcome AI as a classroom companion. Use it for data analysis, but remember that emotional support, values transmission, and ethical guidance remain distinctly human strengths.

Accountability: Teach digital proficiency alongside human literacy. Insist on transparency from AI developers, guard data privacy, and integrate AI ethically into educational practice.

The Human Heart Of Education

AI cannot and should not replace the multidimensional tapestry of human teaching. By learning to navigate a world where AI and NI coexist, we can build a more effective educational approach. When AI's computational strengths are harnessed to support and amplify human wisdom rather than overshadow it, we create learning environments with greater possibilities.

Teachers matter now more than ever — not primarily as knowledge providers, but as champions of human values, emotional intelligence, and critical thinking. In an age where AI-generated content becomes increasingly convincing, a teacher's empathy, ethical judgment, and ability to inspire curiosity become our most valuable educational resources. Those teachers that show the path to HI in practice offer their students the gift of a mindset that makes them future-proof.

Practical AI: A Low-Cost, Low-Risk Guide To Getting Started

According to Gartner, ninety percent of CIOs say AI's prohibitive costs are a limiting factor in AI ... [+] success. Leaders need a low-budget, practical AI approach that allows secure exploration of AI's benefits.

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According to Gartner, ninety percent of CIOs say AI's prohibitive costs are a limiting factor in AI success. Another report, this one from KPMG, found that 68% of CEOs identify AI as an investment priority.

In other words, leaders are in a bind. They're under pressure to implement AI, quickly prove the technology's value and avoid the governance and security risks, all while figuring out how to shoulder the prohibitive cost for deploying AI across the organization. For many, it feels like an impossible math equation that just won't work.

But what if there was another way to get started with AI that lowers the investment costs, demonstrates ROI and avoids the security risks? Executives need to go back to the basics, exploring how AI can solve specific business problems. AI agents—which can range from simple chatbots to process agents orchestrating and improving a process to complex adaptive systems working autonomously toward a goal—offer a promising road forward.

Read on for a practical AI approach that allows leaders to capitalize on AI's groundbreaking benefits and test out AI agents securely—all without breaking their budget or embarking on a complicated year-long transformation project.

The shift to an AI-powered world has many leaders feeling overwhelmed. The technology is too powerful, moving too quickly and is too important to the organization's future success. All those factors make it vitally important to get AI implementation right. That's a lot of pressure.

Leaders need a small perspective shift. True, many companies are prioritizing AI investment, and some have successfully deployed the technology enterprise wide. But most are in the same boat: Still experimenting and figuring out how the technology best fits within the organization. Leaders may feel behind, but it's a solid bet that most everyone else does, too.

Going all in on one AI tool or deploying it across the entire enterprise isn't necessary or recommended at this stage. The important thing is to get started in whatever small ways make sense for the organization, its budget and goals. Here's how:

Start with use cases. Before exploring specific AI tools, identify a few practical AI use cases. What are some problems AI can help solve right now? One way to go about this is to identify bottlenecks in the organization. Here are a few common ones:

The valuable yet overextended team expert who has deep, niche knowledge. Requests for information can easily take these experts a month or two to address.

Routine admin tasks that eat up too much time and cause project slowdowns.

Software developers with too many requests for tedious tasks such as helping write test cases or document code.

IT teams that resist creating and maintaining endless data reports for business performance managers.

Senior team members responsible for passing information along to more junior employees, such as senior mechanics communicating how to fix an issue, or IT leaders answering questions about IT systems.

All these scenarios would likely benefit from an AI assist, such as a chatbot designed to speed up the transfer of information. Not only would this empower team members to quickly get the information they need, but it would also give overextended experts more time to focus on their most valuable work.

Run limited-scope pilots. To prove AI value, choose a use case and run a small, limited-scope pilot. This tactic offers a few benefits. The pilot's small scale means lower costs. Limiting the pilot to one or two use cases makes it easy to measure and track ROI, and it can be done in a matter of weeks, leaving businesses with a quick, flexible experiment in how AI can benefit their organization. Rinse and repeat, building up internal expertise in AI along the way.

One of my company's clients recently had success with a limited-scope AI pilot. An energy and utility company was bogged down with a costly, labor-intensive and slow process to inspect and maintain thousands of critical valves. These valves come in a variety of configurations from multiple manufacturers. Quality control technicians had to comb through hard-copy or PDF manuals to find the appropriate specs for each valve.

For the pilot project, the energy and utilities company focused on one manufacturer. My company deployed a proprietary AI agent to extract the appropriate information from the documents and build a user-friendly database. This allows quality control technicians to access specs much more quickly. The company picked a small area for AI experimentation, and now that the value is apparent, they are well positioned to repeat the process with other manufacturers.

Limit risks by creating a walled garden test atmosphere. Leaders can design narrow-scope AI pilots to operate completely within a walled garden environment. Designed this way, only a small number of people will have access to the data and the AI tool—ideally those who already have access to the data, such as the CFO and other top executives.

This setup limits the security and governance risks, providing a more secure way of testing the technology. It also gives leaders an opportunity to hone AI skills they can pass on to their direct reports once AI is more widely distributed throughout the company.

Creating a walled garden atmosphere does come with its own challenges. For instance, running AI within a closed environment means the AI tool can work only with a small set of data, rather than benefiting from data from the entire internet.

However, there are workarounds. Engineers from my company, Centric Consulting, have created accelerators (pre-built code or software tools) to solve this challenge in different toolsets. Whether an organization deploys AI internally or works with a third-party vendor, it's important to first explore how a walled garden atmosphere might impact the use case, performance and cost.

It's OK To Start Small With Practical AI

AI will undoubtedly change the business world in unimaginable ways. It's a big technology with big promises. But that doesn't mean companies have to figure it all out now. By starting with a small, narrow pilot project to determine clear ROI with AI or AI agents, leaders can get practical AI experience and prove value quickly—without a hefty budget.

Bringing AI To English Lessons

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