On July 4, 2024, the Ministry of Education of Spain and INTEF presented to the public the report titled “AI in Education: Challenges and Opportunities“. We have taken the time to generate an 8-page analysis document which, thanks to my “copilot” Claude 3.5, has been summarized, with additional revisions by us, as follows:
Definition and Context
The document begins with a very general definition of different AIs, omitting the definition of AI that currently concerns the education system the most, that is, generative AI, which is defined as computational techniques capable of generating new and meaningful content from training data (Feurriegel et al, 2023). Its role in education is unavoidable, requiring understanding from the entire educational community. This definition unfortunately does not appear in the original text.
AI in Education
The administration recognizes the unavoidable role of AI in education, emphasizing the need for the entire educational community to understand its functioning and applications. Critical questions are raised about how AI can improve teaching-learning processes and transform the roles of teachers and students. Although the need for a regulatory framework is recognized, in my opinion, it should be questioned whether this is sufficient to address all the challenges that AI presents in education, or if new educational paradigms are required. The importance of fostering critical and reflective thinking is emphasized so that society can understand and maximize the potential of these technologies in various ethical-political contexts.
Implications and Concerns
The report emphasizes these three points as the administration’s main concerns:
- Privacy of student data
- Biases in results and equity in access
- Ethical and inclusive use
Key Questions
It can be inferred that the main key questions of the document are the following:
- Can AI improve teaching-learning processes?
- How does education change, along with the role of the teacher and the student?
- Is a regulatory framework sufficient or are new paradigms needed?
AI Approaches in Education
- Teaching for AI: Development of competencies to face challenges that the tool itself entails.
- Teaching about AI: Technical approach and effective use to maximize results in all approaches and roles.
- Teaching with AI: Integration in the teaching-learning process from both a technical and methodological and administrative perspective.
Role of Teachers
Challenges
- Anxiety due to rapid technological evolution
- Fear due to the perception of dispensability and replacement
- Threats to privacy and security of student data, especially when they are minors
- Limitations in access and racial, gender, etc. biases of AI tools
Strategies
- Continuous and flexible training and capacity building
- Promotion of autonomy for teachers to be innovative and researchers
- Establish guarantees of privacy and security of student data
- Quality review of AI contents and tools
Applications
- Resource creation: Task automation and content personalization
- Learning personalization: Data analysis and predictive models for vocational guidance
- Evaluation: Automated feedback and automated and personalized evaluation
- Administrative management: Document translation and facilitation of bureaucratic processes
Role of Students
Challenges
- Erroneous assumption about the lack of AI competencies in students, at least at a technical level
- Concern about “dependence” on AI, possibly misinterpreted due to the administration’s fear that its methodologies may become outdated
- Vulnerability of personal data
Strategies
- Promotion of autonomy and critical thinking in relation to new tools
- Promotion of data protection policies
- Design of inclusive and equitable AI systems (Proprietary to the administration?)
Students as Creators and Consumers
- As consumers of study materials and, at most, as adapters of them, but never as co-creators 😞
- Beneficiary of a new type of adaptive and personalized tutoring
- Implementation of virtual assistants and learning management systems (LMS) in the classroom
Administrative Staff
Challenges and Limitations
- Immobility and resistance to change, possibly due to… Fear of acknowledging certain professional obsolescence?
- Awareness of the administration’s responsibility in the accessibility of technologies in the classroom
- Lack of academic studies on the improvement of learning with AI
Strategies and Preventive Measures
- Implementation of workshops and continuous teacher training
- Investment in technological infrastructure… That allows for a “public administration proprietary AI”?
- Fluid communication between administration and academia to implement new improvement processes
- New? digital literacy programs for the entire educational community
Educational and Professional Orientation for Students
- Recommendation systems and vocational guidance based on predictive models of labor demand
- Simulations for job presentations and interviews
- Connection with mentors and industry professionals based on both demand and student profiles
Process Management and Automation
- Optimized schedule planning
- Analysis of demographic trends for enrollment projections
- Evaluation of educational centers
On the Decalogue of Good Use
The decalogue for the use of AI in education, although well-intentioned, reveals some contradictions and challenges not fully explained.
While respectful integration, transparency, equity, and robustness should be the primary responsibility of the providers of these AI systems, the need for a proprietary system controlled by the administration and in close collaboration with technical professionals and academic experts becomes evident.
Privacy and data protection not only implies administrative management but also teaching students to use their data critically and for their own benefit.
Human supervision and compatibility should foster critical thinking, making students aware of the biases and limitations of these tools, as well as the need to respectfully horizontalize power hierarchies in the classroom so that such critical thinking does not encounter barriers that prevent its development.
Promoting social well-being through AI seems like an oxymoron in a world dominated by commercial interests, but critical thinking should be oriented towards creative and socially beneficial uses of technology.
It is crucial to combat potential isolationism by fostering collaborative learning.
Finally, continuous reflection on the ethical impact of AI must translate into concrete mechanisms for two-way communication between the administration, the educational community, and families, using AI itself to manage this process efficiently.
General Conclusions and Critical Reflections
- The document is introductory and probably of limited scope.
- Students are still relegated to the limitations of teachers when they should be able to have equal or superior AI skills as co-creators and not just as consumers or adapters.
- The feeling of students’ “dependence” on AI could reflect the resistance to change in the educational system and its fears of becoming aware of the obsolescence of certain roles and the birth of new ones.
- Student data could be an opportunity for self-knowledge and vocational guidance if managed properly.
- The current curriculum may not be flexible enough for true personalization of adaptative learning.
- The ban on mobile phones in the classroom contrasts with the need to implement technological infrastructures when students’ devices could mitigate part of the deployment of such infrastructure.
- Digital literacy should go beyond the introductory, fostering critical thinking and creativity in the use of technologies.
- To foster critical and reflective thinking, it is crucial to start by questioning the existing power hierarchies in the educational system.
- AI in Education poses challenges that require a flexible positioning and honest self-criticism with all the consequences.
- It is necessary to acquire skills to face challenges and take advantage of opportunities, always with critical thinking and creativity mentioned previously.
- The effective use of AI involves generating meaningful prompts, an increasingly complex task that requires constant specialization.
- The integration of AI must consider the diversity of contexts and types of educational agents and students.
- It should be questioned whether a legal regulatory framework is sufficient or if additional didactic methodological frameworks are also needed.
Conclusions and Critical Reflections by Roles
Teachers
- Facing rapid technological evolution is a huge challenge considering the current tasks already faced by teachers, but there is the…
- …Possibility of freeing teachers from mechanical tasks thanks to AI to better prepare them for the technological (r)evolution.
- Enhance their role to become mentors for students using predictive models resulting from the interpretation of student data.
- Need to foster autonomy and innovation from the administration.
Students
- It is necessary to generate in students an awareness, a questioning of the perception of their AI skills while fostering their innate ease of use and teachers are humble enough to admit it and learn from it.
- Promote new roles to be able to move from mere consumers to co-creators of classroom content.
- Importance of learning to protect, manage, use, and exploit their own data to endorse meaningful vocational guidance.
- Promotion of critical thinking and horizontal management of power in the classroom for such thinking to flourish.
Administration
- Real and significant efforts must be made to address immobility and resistance to change, which also has important political implications.
- Consider possible job obsolescence and also new roles.
- Invest in accessible and ethical technological infrastructure, a “proprietary” AI at the service of the educational community which entails…
- …Involving the educational community in the development and maintenance of proprietary, public, and open AI tools
Call to Action
All members of the educational community are invited to reflect on how they can contribute to this paradigmatic change enriching the teaching experience, fostering active student learning, and improving public administration management.
It should be crucial to recognize the connection between education and the future of work, preparing students for a world where AI is not only a tool but also a competitor. This implies being critical of the current educational system and vocational options, recognizing that certain professions could disappear or be significantly reduced, such as in the areas of Administration, Logistics, and Manufacturing. According to studies (Frey & Osborne, 2017; Manyika et al., 2017), while an increase in demand for creative professions is predicted (it is not necessarily accompanied by better salaries). This scenario demands a deep reflection on how to adapt education to prepare students for these imminent changes in the labor market.
References
- Feuerriegel, Stefan and Hartmann, Jochen and Janiesch, Christian and Zschech, Patrick, Generative AI. (2023). Business & Information Systems Engineering, Available at SSRN: https://ssrn.com/abstract=4443189 or http://dx.doi.org/10.2139/ssrn.4443189
- Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
- Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Sanghvi, S. (2017). What the Future of Work Will Mean for Jobs Skills and Wages. McKinsey Global Institute, 1-21. Retrieved from https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages