The intersection between Artificial Intelligence (AI) and education is emerging as a revolutionary force, transforming traditional pedagogical paradigms and expanding the possibilities for personalized learning. The incorporation of AI into educational structures brings with it the expectation of revolutionizing the way knowledge is transmitted and acquired, making education more adaptive, personalized, inclusive and effective. With its learning and adaptation capabilities, AI will reconfigure the way we teach and learn, promoting a revolution in the educational sector.
The potential of AI in education
A recent study by the company Cortex Intelligence shows that only one in ten recent graduates from the most sought-after undergraduate courses – such as Law and Nursing – can find a job equivalent to their training. The distance between academia and market needs tends to worsen, as 85% of the jobs that will exist in 2030 have not even been “invented”, according to the Institute for the Future.
In this scenario, how can we prepare young people for the job market of the future? I don’t have the answer, but I believe that Artificial Intelligence can be a great ally in facing this challenge. Back in the distant year of 2019, a survey carried out by Microsoft in partnership with Times Higher Education showed what we see today: almost 90% of those interviewed believed that AI would have a significant or very significant impact on curricula and Pedagogy. And that was before the earthquake caused by generative AI in all sectors of society, including education.
Researchers¹ claim that the integration of AI in education is already redefining traditional teaching and learning methods. Personalization of learning, enabled by AI algorithms, adjusts educational experiences to the needs, preferences and pace of each student, surpassing the traditional standardized model and promoting a more inclusive and effective learning environment.
Is this the way to prepare professionals for professions that do not yet exist?
Personalized learning
Personalized learning is one of the most promising areas of applying AI in education. Smart tutoring systems, educational chatbots, virtual tutors, and augmented reality platforms can provide adaptive instruction, adjusting the pace and content to individual students’ needs. Tools like these help identify students who need closer attention and monitoring, provide personalized feedback, and redirect students to personalized reinforcement materials when necessary.
AI can also play a crucial role in including students with specific needs. AI tools can help students who are blind or visually impaired through real-time image descriptions, while students with hearing impairments can benefit from automatic captions and sign language translations. Additionally, AI can provide emotional and practical support by monitoring signs of stress and offering timely interventions.
In relation to learning analysis and tracking, AI can be used to collect and analyze data on student progress. Automated assignment correction and assessment platforms (robot-graders) help provide quick and accurate response to activities, allowing teachers to focus more on developing effective pedagogical strategies.
AI and search
Artificial Intelligence is transforming academic research in significant ways. AI-based research tools are capable of analyzing large volumes of data, identifying patterns and relationships that might otherwise go unnoticed by human researchers, and even assisting in writing and presenting results. Techniques such as machine learning and deep learning allow advanced analysis of complex data, revealing insights that can lead to important advances in various areas of knowledge.
Furthermore, AI plays a crucial role in research aimed at the Sustainable Development Goals (SDGs). It can be a powerful tool in the search for solutions to global challenges, such as poverty, inequality and climate change, as I have already discussed in another article here at MIT Technology Review. AI algorithms can help optimize resource distribution, predict natural disasters, and develop sustainable technologies. However, it is essential to address the ethical risks and implications of using AI in research. Issues such as algorithmic bias, data privacy, accountability and transparency must be carefully considered to ensure that the impact of AI is positive and appropriate. Integrating AI into academic research not only accelerates the discovery process but also expands the scope and depth of investigations.
With the ability to process and analyze data on an unprecedented scale, AI allows researchers to explore new frontiers of knowledge and make connections that were previously unimaginable. However, it is essential that this tool is used correctly, so that its benefits are fully realized without compromising the fundamental values of scientific research.
AI and the future of work
The growing relevance of Artificial Intelligence (AI) in the job market is creating a significant demand for professionals skilled in AI and machine learning. To meet this need, educational institutions, especially – but not exclusively – higher education institutions, need to adapt their curricula, preparing students for this new scenario. In addition to technical skills, it is essential that students develop meta-skills, such as creativity, critical thinking, emotional intelligence, curiosity, autonomy and problem-solving skills. These skills will be essential for professionals to adapt and thrive in an ever-changing job market. Higher education must also promote lifelong learning, offering programs that allow professionals to continually update their skills and knowledge.
AI can play a crucial role in this process, facilitating online and distance education and ensuring that more people have access to educational opportunities. With the help of AI, it is possible to create more flexible and accessible learning environments, allowing individuals to continue to develop professionally throughout their careers. The integration of AI in higher education not only prepares students for the technical demands of the job market, but also helps develop the skills needed to face the challenges of a constantly evolving world. By promoting continuous and adaptive learning, AI can help create a more resilient and future-ready workforce.
Challenges for AI in education
Despite the many advantages, implementing AI in higher education faces several challenges. Inequalities in access to technology, privacy and data security issues, and the risk of bias in AI algorithms are some of the obstacles that need to be overcome.
The uneven distribution as well as the heterogeneity of AI maturity globally – and even within a country – is a significant challenge. Regions with limited resources face difficulties accessing and implementing AI technologies, exacerbating existing inequalities.
Furthermore, the use of AI in education involves the collection and analysis of large volumes of students’ personal data. It is essential to establish robust privacy and data security policies to protect this information and guarantee the ethical use of AI, to ensure compliance with legislation and regulations that deal with the topic, such as GDPR and LGPD.
The third point of attention I highlight – and there are certainly several others – arises from the principle that AI algorithms are only as good as the data on which they are trained. Thus, if data is biased, algorithms can perpetuate and even amplify these biases, leading to unfair and discriminatory decisions. Therefore, it is essential to develop strategies to identify and mitigate these biases when using AI, especially in education.
The ethical use of AI in education
UNESCO highlights the importance of addressing the ethical issues associated with the use of AI in education. The “Recommendation on the Ethics of Artificial Intelligence”, adopted by UNESCO in 2021, establishes a framework for the development and ethical use of AI, which must be guided by principles of academic integrity. This includes ensuring that AI systems are transparent, fair and used to promote human development and well-being. To achieve this, educational institutions must develop clear policies and guidance on the use of AI, addressing issues such as acceptable use, data privacy, algorithmic bias and cybersecurity. In this context, UNESCO suggests some steps for the responsible integration of AI into the teaching-learning process:
- Internal capacity building: Develop training programs for teachers and administrators to understand the applications and implications of AI.
- Development of an AI Action Framework: create policies and frameworks to guide the use of AI in institutions.
- Innovation in pedagogical and skills training: integrating AI into the curriculum so that students develop both technical skills and meta-skills.
- Promoting AI research and application: encouraging interdisciplinary research and the development of new AI applications in education.
- Mobilizing knowledge and communities around AI: foster collaboration between institutions and communities to share knowledge and best practices.
- Promoting gender equality in AI and higher education: Implement policies to increase the participation of women and other underrepresented groups in AI research and development.
The AI revolution in education is just beginning, but its impact is already profound and far-reaching. From personalizing learning to optimizing institutional administration, AI offers countless opportunities to improve higher education. However, it is crucial to address the challenges and ethical issues associated with the use of AI to ensure that it is used in a fair, inclusive and responsible way.
Higher education institutions have a vital role to play in leading this transformation by preparing students for the future of work, promoting interdisciplinary research, and developing robust policies for the ethical use of AI. With a thoughtful and informed approach, AI can be a powerful force for good in education, helping to achieve the Sustainable Development Goals and promoting a more equitable and sustainable future for all.
( fonte: MIT Technology Review )
