Pitfalls and Risks of AI in Education

The integration of artificial intelligence in education offers numerous opportunities, but also involves significant risks that should be identified and prevented. This article explores the main pitfalls to avoid.

Excessive Trust in AI Results

The Myth of Algorithmic Infallibility

One of the first pitfalls is placing excessive trust in results provided by AI:

• AI systems can generate incorrect or approximate information
• Language models can “hallucinate” facts that seem plausible but are erroneous
• Biases present in training data are reflected in the results

Recommendation: Adopt a systematic verification approach and maintain a critical eye on content generated by AI.

Technological Dependency

Atrophy of Fundamental Skills

Excessive or poorly framed use of AI can lead to:

• A decrease in autonomous thinking abilities
• A weakening of research and information evaluation skills
• A reduction in perseverance when facing intellectual challenges
• A loss of original creativity

Recommendation: Clearly define when AI is an appropriate tool and when personal cognitive effort is essential to learning.

Inequalities in Access and Skills

The Risk of an Aggravated Digital Divide

The integration of AI can amplify existing inequalities:

• Disparities in access to technologies between institutions and students
• Differences in digital literacy among teachers
• Competitive advantage for learners who master these tools
• Marginalization of less technological educational contexts

Recommendation: Implement policies for equity of access and training programs for all educational stakeholders.

Dehumanization of the Pedagogical Relationship

The Risk of Excessive Automation

An overvaluation of AI can lead to:

• A reduction in human interactions essential to learning
• Excessive standardization of educational pathways
• Neglect of socio-emotional dimensions of education
• A technocratic and reductive vision of learning

Recommendation: Design AI as a tool in service of the pedagogical relationship, not as its substitute.

Ethical and Privacy Issues

Protection of Sensitive Data

The use of AI raises important ethical questions:

• Collection and use of learners’ personal data
• Potential surveillance of learning behaviors
• Algorithmic profiling of students
• Limited transparency of systems used

Recommendation: Develop clear ethical frameworks and prioritize privacy-respecting solutions.

Facilitated Plagiarism and Cheating

Threatened Academic Integrity

Generative AI tools can facilitate:

• Production of non-original work
• Circumvention of traditional assessments
• Difficulty in distinguishing the learner’s personal work
• Questioning of classical evaluation methods

Recommendation: Rethink evaluation methods and educate on the ethical use of AI.

Conclusion: A Thoughtful and Balanced Integration

Faced with these risks, the wisest approach is to adopt a balanced posture:

• Neither naive technophilia ignoring potential pitfalls
• Nor reactionary technophobia rejecting real benefits

Successful integration of AI in education requires continuous reflection, ethical vigilance, and constant adaptation of ped