The AI education dilemma: Knowledge exists, but practical classroom application falls short

Despite AI’s growing presence in education, its implementation in rural schools is still in its early stages. The study found that most teachers who use AI in their practice rely on it for text-based content creation and verification. AI-generated lesson plans and automated grading tools help save time, but deeper, more interactive AI applications such as adaptive learning systems and AI-driven tutoring platforms remain largely unexplored.

The integration of artificial intelligence (AI) and data literacy into rural schools continues to be a challenge. A new study, "Artificial Intelligence and Data Literacy in Rural Schools’ Teaching Practices: Knowledge, Use, and Challenges," published in Education Sciences (2025), explores the level of AI awareness, its applications in rural schools, and the barriers preventing full adoption. The research, conducted in Catalonia, Spain, surveyed teachers to assess their knowledge of AI tools, how they currently use them in the classroom, and their concerns about integrating AI into teaching.

The findings paint a complex picture: while more than half of the surveyed teachers reported having moderate to high AI knowledge, their actual use of AI in classroom settings remains limited. AI is primarily used for text generation and content detection, but applications such as video generation, predictive analytics, and AI-driven personalized learning remain underutilized. Moreover, teachers expressed concerns about ethical issues, academic integrity (plagiarism and cheating), and a potential decline in students’ critical thinking skills.

How AI is currently used in rural schools

Despite AI’s growing presence in education, its implementation in rural schools is still in its early stages. The study found that most teachers who use AI in their practice rely on it for text-based content creation and verification. AI-generated lesson plans and automated grading tools help save time, but deeper, more interactive AI applications such as adaptive learning systems and AI-driven tutoring platforms remain largely unexplored.

Teachers primarily integrate AI into their work through:

• Lesson planning: AI assists in generating structured lessons, automating material creation, and suggesting relevant resources.
• Content verification: AI is used to detect plagiarism and assess AI-generated content, helping teachers maintain academic integrity.
• Basic automation: Some educators utilize AI-driven chatbots for answering student queries and automating administrative tasks.

However, more advanced uses, such as AI-powered simulations, predictive analytics for student performance, and personalized adaptive learning, are rare. This reflects a disconnect between teachers' theoretical understanding of AI and their practical ability to implement it effectively.

Key challenges preventing AI adoption in rural schools

While AI has the potential to revolutionize rural education, several barriers limit its widespread adoption. The study identifies four major challenges:

Limited access to AI training and resources

One of the most significant challenges is the lack of AI training programs tailored for rural educators. While urban schools often have better access to AI workshops, digital infrastructure, and professional development opportunities, teachers in rural areas struggle to find AI-specific training that fits their needs.

Without proper training, many educators lack the confidence to integrate AI into their teaching strategies, leading to a gap between AI knowledge and its real-world application.

Ethical concerns and academic integrity issues

Teachers expressed concerns about plagiarism, cheating, and AI’s impact on students’ ability to think critically. With AI-generated content becoming more sophisticated, some fear that students may rely too heavily on AI tools instead of developing their problem-solving skills.

Additionally, there is a growing debate over data privacy - schools must ensure that AI-driven tools comply with ethical standards and student data protection policies.

Digital divide in rural communities

Many rural schools lack the necessary infrastructure - such as high-speed internet, cloud computing access, and AI-compatible devices - to fully implement AI-based learning solutions. This digital divide makes it challenging for students and teachers in rural areas to access the same technological opportunities as their urban counterparts.

To bridge this gap, policymakers need to prioritize digital infrastructure investments in rural education and provide funding for AI-enabled learning tools.

AI’s role in teaching versus traditional learning approaches

Some educators believe that AI should complement, not replace, human instruction. While AI can automate tasks and offer personalized recommendations, teachers emphasize that human connection, creativity, and mentorship remain irreplaceable in education.

This raises an important question: How can AI be integrated without undermining the human aspects of teaching? The study suggests that blended learning models, where AI supports but does not replace teachers, may be the best approach for rural education.

Future of AI and data literacy in rural schools

Despite these challenges, the study predicts that by 2030, AI adoption in SME education will significantly increase, driven by more accessible AI solutions, improved digital infrastructure, and government-led initiatives. Several key trends are expected to shape the future of AI in rural schools:

• AI-Powered Digital Tutors: AI-driven tutoring systems will provide personalized learning pathways, especially for students who lack access to specialized educators.
• Edge AI for Low-Connectivity Areas: AI that operates without constant internet access will become more common, making it easier for rural schools to adopt AI-driven tools.
• AI in Professional Development: AI-based training programs for teachers will enhance digital literacy skills and help educators implement AI-based teaching strategies more effectively.
• Government and Private Sector Support: Increased investments in AI-enabled education will help rural schools access high-quality learning resources and AI-driven platforms.

If implemented effectively, AI has the potential to bridge the education gap between rural and urban schools, enhance personalized learning experiences, and empower teachers with data-driven insights to improve student outcomes.

What's next?

To fully harness AI’s potential in rural education, educators, policymakers, and technology providers must work together. The study suggests several strategies:

• Providing AI training for teachers: Schools should invest in AI literacy programs to ensure educators feel confident using AI in their classrooms.
• Developing AI tools tailored for rural settings: AI solutions should be lightweight, accessible offline, and easy to integrate with existing teaching methods.
• Addressing ethical concerns and academic integrity issues: Schools need clear policies on AI use in education, ensuring that AI tools support, rather than undermine, student learning.
• Bridging the digital divide: Governments must prioritize technology investments in rural schools to ensure equal access to AI-enabled education.

With the right approach, AI can become a powerful tool for rural education, transforming the way students learn and teachers teach. By addressing the current challenges and focusing on inclusive AI adoption, rural schools can unlock new opportunities for innovation and academic success.