INTELIGENCIA ARTIFICIAL EN LA EDUCACIÓN: RETOS QUE NO PUEDES IGNORAR🚀🤖

Inteligencia Artificial y los Desafíos en la Educación

Introducción a la Inteligencia Artificial en Educación

• La conferencia aborda los desafíos que presenta la inteligencia artificial (IA) en el ámbito educativo, incluyendo aspectos filosóficos, pedagógicos y de recursos digitales.

• Se presentan siete puntos clave: introducción a la IA en educación, desafíos de modelos educativos, docencia, investigación, formación universitaria, ética y sociales.

Panorama Actual de la Inteligencia Artificial

• La IA no es solo un conjunto de herramientas; se trata de reimaginar el aprendizaje donde la tecnología amplifica las capacidades humanas.

• Se desmiente el mito de que la IA reemplazará a los docentes; se enfatiza que son necesarios educadores que comprendan cómo utilizarla para innovar.

Capacidades y Funciones de la Inteligencia Artificial

• La educación no debe reducirse a una simple transferencia de información; es crucial entender que educar implica más.

• La IA puede resolver problemas, tomar decisiones y aprender. Estas características son fundamentales para su aplicación educativa.

Evolución y Tipos de Inteligencia Artificial

• Antes del surgimiento de la IA generativa en noviembre 2022, existían tecnologías como teléfonos inteligentes y electrodomésticos programables basados en algoritmos predefinidos.

• Con el avance hacia sistemas generativos como ChatGPT, se abre un nuevo horizonte donde estos pueden crear nueva información basada en datos previos.

Interacción Humana con Sistemas de Inteligencia Artificial

• Los usuarios mejoran continuamente los sistemas al interactuar con ellos; cada interacción contribuye al aprendizaje del sistema.

• Las principales inteligencias artificiales actuales incluyen OpenAI's ChatGPT, Google Copilot y Anthropic's Claude. Su competencia beneficia a los usuarios mediante mejoras constantes.

Reacciones Institucionales ante la Inteligencia Artificial

Global Debate on Artificial Intelligence in Education

Introduction to AI in Education

• The global debate on the use of artificial intelligence (AI) in education has begun, emphasizing that AI is not merely a set of tools but an evolving approach to life.

• Various entities, including the European Union, the United States, and UNESCO, are advocating for regulations regarding AI's role in educational settings.

Current Challenges and Concerns

• There are significant concerns surrounding AI in education, such as plagiarism fears due to tools like ChatGPT and worries about AI replacing teachers.

• Ethical issues arise from a lack of regulation concerning data privacy and security, leading to potential desensitization within educational environments.

Regulatory Developments

• The European community has initiated regulatory measures for AI; a law was published that includes immediate prohibitions on facial recognition databases.

• New regulations require transparency from companies developing AI systems regarding their training data and associated risks.

Educational Model Transformation

• A major challenge for educational models is adapting curricula to integrate technology while fostering citizens capable of embracing change.

• The COVID-19 pandemic highlighted the need for refreshing educational models; professionals now require competencies that were previously unrecognized.

Dimensions of an Educational Model

• An effective educational model comprises four dimensions: philosophical, pedagogical, social, and organizational.

• Philosophical Dimension: Involves principles and beliefs guiding the implementation of an educational system.

• Pedagogical Dimension: Based on scientific theories that inform teaching methods within universities.

Understanding the Impact of Artificial Intelligence in Education

The Need for Organizational Change in Education

• The current educational model must adapt to societal changes, particularly with the integration of artificial intelligence (AI). This requires a reevaluation of organizational dimensions within educational institutions.

• Ethical considerations and comprehensive training are essential in today's educational landscape, especially post-COVID. Institutions need to reassess their foundational documents like institutional educational projects and internal regulations.

Addressing Challenges Post-COVID

• There is a pressing need to update internal regulations based on experiences from COVID-19. Institutions must address new risks associated with AI, such as plagiarism and misuse of information.

• Ethical committees at universities should manage these risks effectively, highlighting the significant impact that AI has on education.

Evolution of Pedagogical Mediation

• Historically, pedagogical mediation involved a simple interaction between teacher, student, and content. However, technological advancements have transformed this dynamic significantly.

• Today's learning environments are more complex; students now choose how they engage—whether in-person or virtually—which alters traditional teaching methods.

The Role of Educators in an AI-driven World

• As AI becomes more prevalent, educators face challenges regarding ethics and data privacy. There's a risk that technology could dehumanize learning experiences.

• Despite advancements in technology, human interaction remains crucial. Teachers must leverage AI tools effectively to enhance learning rather than replace personal engagement.

Inequities in Access to Technology

• Disparities exist in access to technology across different regions (e.g., urban vs rural areas), which poses challenges for equitable education models.

• Updating curricula and enhancing digital competencies among teachers are vital steps toward addressing these inequities while integrating AI into education.

Understanding Technological Dependencies

• A historical perspective on web evolution illustrates how reliance on technology can lead to misconceptions about its role in education (Web 1.0 vs Web 2.0).

• Terms like "teacher 2.0" reflect an increased interactivity with technology but may misrepresent the actual needs for effective teaching practices.

Importance of Neuroeducation Awareness

• Educators must understand how students learn; this knowledge is critical for effective teaching strategies amidst evolving technologies.

• Misuse of terminology related to neuroeducation can detract from its importance; understanding cognitive processes is essential for improving teaching methodologies.

Conclusion: Navigating Future Educational Landscapes

Challenges in Educational Models and Integral Formation

Importance of Integral Education

• The speaker discusses the challenges posed by educational models, emphasizing that integral formation is fundamental for student development.

• Different levels of students (early, mid-cycle, graduating) require tailored guidance, highlighting the necessity of personalized attention in education.

Curriculum Adaptation

• John Franklin Bobbitt's assertion that curricula must adapt to social needs is referenced; he is recognized as a pioneer in modern curriculum theory.

• The Peruvian Constitution (1993) and Law 28044 stress the goal of education as the comprehensive development of individuals.

Legislative Framework Supporting Integral Education

• Article 13 of the Constitution emphasizes education's role in human development; Law 28044 defines education as a lifelong learning process contributing to integral formation.

• The University Law (30 220) mandates teachers to provide tutoring for professional development, reinforcing the importance of mentorship.

Quality Assurance in Higher Education

• The Supreme Decree 016 outlines that quality assurance policies aim to ensure all youth have access to quality higher education with an emphasis on integral formation.

The Role of Artificial Intelligence in Education

AI's Impact on Teaching

• While AI will not replace educators, it enhances their capabilities and amplifies their impact when integrated effectively into teaching practices.

• AI can serve as a tool for information access but requires educators to transform it into an innovative learning resource.

Understanding AI Limitations

• A distinction is made between human reasoning and AI decoding; while AI can process language, it lacks true comprehension and may make errors due to its limitations.

Dimensions Required for Integral Education

Multifaceted Approach to Learning

• An integral education necessitates cognitive, emotional, social, and ethical dimensions within teaching methodologies.

• Educators must balance their roles as instructors with those as guides or mentors without compromising academic rigor or expectations.

Challenges with Learning Management Systems (LMS)

Evolution of LMS Platforms

• Many university educators have experience using various LMS platforms like Moodle and Chamilo which are designed for managing learning processes effectively.

• Despite being around for over two decades, these platforms still need improvements regarding personalized learning experiences and adaptive curricula.

Future Trends in Education Technology

Mathematics Learning Platforms and Adaptive Learning

Overview of Mathematics Learning Platforms

• The speaker discusses the use of mathematics learning platforms in various countries, including Mexico, the United States, Colombia, and Peru. These platforms are utilized by some private schools but are considered somewhat expensive.

• An example mentioned is "Alex," which serves as a platform for adaptive learning in mathematics.

Features of Adaptive Learning Platforms

• The speaker explains that when students enter an adaptive learning platform, they select their course and grade level. The platform then administers an initial exam to assess their knowledge.

• Teachers can prepare resources within the platform; however, it automates many processes such as administering entrance exams without requiring teacher presence.

Personalized Learning Paths

• If a student does not complete the exam or leaves questions unanswered, the system uses artificial intelligence algorithms to identify gaps in knowledge.

• This allows for personalized learning itineraries; if 25 students log in, there could be 25 different paths tailored to each student's needs.

Limitations of Traditional Classrooms

• The speaker emphasizes that creating individualized learning plans for each student is nearly impossible in traditional classroom settings due to logistical constraints.

• While AI can provide personalized education paths, human educators still play a crucial role that technology cannot replicate.

Integrating Artificial Intelligence into Teaching Practices

• A proposal is made regarding implementing AI in teaching practices. Educators must recognize that students will use AI tools regardless of authorization.

• Establishing clear policies on how AI can assist with assignments is essential for effective integration into educational activities.

Structuring Assignments with AI Consideration

• It’s important to define objectives clearly when incorporating AI into tasks and ensure that activities are sequenced logically to challenge students beyond simple copying from AI sources.

Importance of Reflection and Listening in Education

• All assignments should include reflective components. In virtual classrooms, it's vital to create opportunities for student feedback and discussion during lessons.

Roles of Educators Across Different Scenarios

• The speaker outlines four key roles educators must fulfill regardless of whether they teach virtually or face-to-face:

• Accompaniment: Supporting students throughout their learning journey.

• Intentional Interaction: Engaging with students purposefully to guide their understanding through tutoring and mentorship.

Learning Management and Evaluation Strategies

Content Management in Learning Environments

• The management of learning content is crucial for achieving educational objectives; it involves not just the creation of materials by teachers but also sourcing information from various platforms.

• Effective strategies include problem-based learning, case studies, project-based learning, and flipped classrooms to enhance the learning experience.

Role of Assessment in Education

• Assessment should be an integral part of the teaching process, occurring continuously from the start to the end of a class.

• Evaluations can be diagnostic, formative, or summative; they are essential for guiding both teaching and learning processes.

Andragogy: Adult Learning Principles

• The speaker emphasizes the importance of andragogy—the art and science of helping adults learn—especially relevant in postgraduate education.

• According to Knowles' theory from 1970, adult learners come with defined self-concepts, prior experiences, different motivations, and orientations towards learning compared to undergraduate students.

Key Principles for Postgraduate Education

• Important principles include learner autonomy, leveraging prior experience as a resource, immediate relevance of content, and problem orientation in teaching methods.

• Engaging with experienced professionals (e.g., executives or board members) enriches postgraduate classes significantly compared to traditional undergraduate settings.

Research Challenges with Artificial Intelligence

• AI tools should complement rather than replace human research capabilities; they must be used strategically within academic contexts.

• Traditional information retrieval methods have evolved due to AI algorithms enhancing access to vast databases like Scopus and others.

Utilizing AI Tools Effectively

• Current AI-enhanced tools provide access to millions of documents; educators need to adapt their research methodologies accordingly.

• Tools like Consensus facilitate reading multiple scientific articles efficiently but should not substitute critical thinking or investigative skills.

Addressing Common Questions about AI in Academia

Citing CHAG PT: Is It Correct?

Criteria for Authorship in Biomedical Journals

• The International Committee of Medical Journal Editors outlines four criteria for authorship:

• Significant contributions to the work.

• Involvement in drafting or critically revising the content.

• Approval of the final version for publication.

• Accountability for all aspects of the work.

Responsibility and Citation Debate

• Discussion on whether CHAG PT can be cited as an author, given its ability to take responsibility for the work and decide on publication.

• Raises questions about citing CHAG PT if it does not meet authorship criteria, emphasizing that users must assume responsibility when utilizing such tools.

Challenges in the Era of Artificial Intelligence

Coexistence with Innovation

• Emphasizes that the main challenge is not fearing replacement by AI but learning to coexist with it to enhance human capabilities.

AI Applications in Legal Services

• Highlights various AI applications used in legal contexts, including systems that analyze cases and predict outcomes based on existing laws.

AI's Role in Judicial Decisions

Predictive Tools Used by Judges

• Discusses how judges use AI systems to assess probabilities related to parole decisions, influencing their rulings without formal regulations governing these practices.

The Future of Lawyering with AI

Will Lawyers Be Replaced?

• Asserts that while lawyers will not be replaced by AI, they must adapt and update their skills to utilize these new tools effectively.

AI Enhancements in Medicine

Improving Diagnostic Accuracy

• Describes how AI tools are enhancing medical diagnostics rather than replacing doctors, leading to more accurate assessments through advanced technology.

Examples of Medical AI Tools

• Introduces Docus, an AI service where users can interact with a virtual doctor for preliminary health assessments based on symptoms provided.

Conclusion on Medical Diagnostics

Applications of Artificial Intelligence in Engineering

Overview of AI in Engineering

• The use of artificial intelligence (AI) in medicine and engineering is expanding, with numerous applications emerging. Automation of tasks and optimization of processes are key benefits.

• Intelligent systems development, predictive maintenance, and the need for AI skills across all engineering fields are highlighted as essential components for modern engineers.

Data Management and Sustainability

• The management of large data volumes (Big Data), implementation of collaborative robots in industry, resource optimization, environmental sustainability, and advanced cybersecurity systems are critical areas where AI is making an impact.

• Engineers specializing in cybersecurity are increasingly sought after due to the growing importance of protecting information systems.

Future Work Scenarios

• The future labor landscape will see task automation, transformation of roles and skills, hybrid work models, increased personalization, co-creation between humans and machines, data-driven decision-making, and changes in organizational structures.

Ethical Challenges Posed by AI

Ethical Considerations

• Major challenges associated with AI extend beyond technology to ethical, social, and legal dimensions. Ensuring that progress respects human dignity and promotes equity is crucial.

• Misuse of AI can lead to misinformation, political manipulation, reputational damage, security threats to information privacy—highlighting the necessity for regulation.

Educational Institutions' Role

• Educational institutions must establish internal regulations addressing these risks while fostering responsible use among students.

The Importance of Ethics in AI Development

Philosophical Perspectives on Ethics

• Ethics as a philosophical discipline examines morality which varies culturally; thus understanding diverse moral frameworks is vital when discussing ethics related to AI.

• Professional codes often align with deontological ethics based on Immanuel Kant's ideas about duty derived from reason.

Virtue-Based Ethics

• Aristotle’s virtue ethics emphasizes that ethical behavior stems from personal virtues rather than mere adherence to rules. This perspective encourages individuals to act ethically out of intrinsic motivation.

Transformative Impact on Education

Reimagining Education through AI

• There is a significant educational transformation driven by AI—not merely as a tool but as a catalyst for rethinking educational paradigms.

Adapting Curricula for Change

• Curricula must evolve to prepare citizens for a rapidly changing world influenced by technological advancements; this includes integrating legal considerations alongside ethical discussions regarding AI's impact on education.

Teacher's Role in Integration

• Educators play an indispensable role; they must embrace their position without fear that intelligent systems will replace them while developing competencies necessary for leveraging educational technologies effectively.

Challenges in Scientific Research & Workforce Integration

Early Workforce Integration

Understanding the Risks and Benefits of Artificial Intelligence in Education

The Dual Nature of AI

• Artificial intelligence (AI) presents numerous benefits but also carries significant risks, including misinformation, political manipulation, reputational damage, and misuse. Awareness of these risks is crucial as work models become increasingly hybrid.

Call for Regulation

• There is a pressing need for global regulation regarding AI; however, immediate action can be taken within educational institutions to establish internal regulations.

Invitation to Join Educational Networks

• An invitation is extended to join an international network of educators from Latin America and the Caribbean, which has over 52,000 members across more than 25 countries. This network facilitates collaboration among teachers through scientific committees and congress participation.

Utilizing AI as a Tool

• Educators are encouraged to leverage available resources such as virtual campuses and video conferencing tools to enhance teaching experiences without incurring costs.

Ethical Considerations in AI Usage

• The ethical implications of using AI in education were highlighted, particularly concerning how legal judgments differ when made by humans versus machines.

The Role of AI in Legal Contexts

Interpretation Challenges

• In legal contexts, laws often have a singular interpretation. Countries like Germany are exploring how litigants can adapt their strategies within established processes.

Practical Applications of AI

• It’s important not to view AI systems as finished products; rather they should be seen as tools that assist in creating educational materials like syllabi or presentations.

Distinction Between Creation and Evaluation

• A clear distinction exists between asking AI to create content versus using it for evaluation purposes. For instance, students should engage with their projects actively rather than relying solely on AI-generated outputs.

The Limitations of Current AI Technology

No Universal Intelligence Exists

• There is no universal intelligence capable of generating complete academic works autonomously; human input remains essential throughout the research process.

Importance of Human Effort

• While AI serves as an excellent tool for enhancing productivity in academic work, it cannot replace the critical thinking and creativity required from individuals involved in research or writing tasks.

Regulating AI Use in Basic Education

Institutional Variability

• Regulations governing the use of artificial intelligence vary significantly between educational institutions. Each institution must assess its unique context before implementing guidelines on appropriate usage.

Addressing Misuse

• Institutions should consider potential misuse scenarios when developing regulations around the use of artificial intelligence while promoting its beneficial applications within educational settings.

Engaging Educators Through Demonstration

Opportunity for Practical Examples

Academic Work Preparation and Presentation

Introduction to Academic Document Preparation

• The speaker shares a document in Spanish that outlines recommendations for preparing and presenting academic work in medical journals, emphasizing the characteristics an author should possess.

Course Development Using AI

• The speaker discusses developing a course on artificial intelligence for research and writing aimed at educators, mentioning a syllabus template relevant to previous conference discussions.

• They explain how to instruct ChatGPT to act as an expert university educator in creating a syllabus for a postgraduate curriculum, specifying competencies and capabilities required for the course.

Syllabus Structure and Requirements

• The speaker details the essential components of the syllabus, including general information, graduate profile traits, course summary, competencies, units, pedagogical strategies, resources, assessment methods, and references.

• They emphasize that understanding how to create a syllabus is crucial before requesting one from ChatGPT; otherwise, the output may not meet expectations.

Utilizing AI Effectively

• The speaker notes that while many view AI as merely conversational tools (chatbots), structured instructions yield better results when using AI for educational purposes.

• After inputting their prompt into ChatGPT for generating a syllabus example, they receive two responses containing all requested elements like general information and evaluation strategies.

Evaluation Systems and References

• The generated syllabus serves as a starting point rather than a final product; it requires alignment with institutional evaluation systems and curricular coherence.

• Importance is placed on ensuring that methodologies align with educational models promoted by institutions. Additionally, references must be robust despite challenges in sourcing them through AI.

Research Project Considerations

• The discussion shifts towards common student inquiries regarding research project titles. The speaker stresses that the object of research is more critical than just the title itself.

Causa y Efecto en Investigaciones

Metodologías de Investigación

• Las investigaciones pueden clasificarse según la metodología utilizada: exploratorias, descriptivas, correlacionales y experimentales.

• Dentro de las experimentales, se encuentran preexperimentales (un solo grupo), cuasi-experimentales (dos grupos) y experimentales propiamente dichas.

• En ciencias sociales, generalmente no se realizan investigaciones experimentales puras.

Evaluación de Títulos con Inteligencia Artificial

• Un estudiante presenta un título para su proyecto de investigación que involucra "habilidades gerenciales". Se utiliza inteligencia artificial para evaluar el título.

• El título propuesto es "habilidades gerenciales y clima institucional", lo cual refleja los aspectos principales a analizar.

Objetivos de la Investigación

• Es importante definir claramente el objetivo principal de la investigación al abordar la relación entre habilidades gerenciales y clima institucional en instituciones educativas.

• Se debe considerar si el enfoque es cuantitativo o cualitativo desde el inicio del proceso investigativo.

Proceso de Evaluación del Título

• La IA evalúa si el título seleccionado está alineado con los objetivos planteados, asignando una nota del 0 al 10 y sugiriendo mejoras.

• La IA indica que el título es claro pero puede mejorarse en especificidad y estilo académico; sugiere resaltar la relación entre las variables analizadas.

Sugerencias para Mejorar Títulos

• Se enfatiza que la correlación no implica causalidad; se deben considerar diferentes enfoques sobre cómo las habilidades gerenciales impactan en el clima institucional.

• Se recomienda crear una tabla comparativa con títulos originales y sugeridos para facilitar la evaluación conjunta entre estudiantes y tutores.

Uso de Inteligencia Artificial como Herramienta

• La IA se presenta como una herramienta útil para mejorar trabajos académicos sin reemplazar el esfuerzo humano; su uso debe ser complementario.

Análisis e Interpretación Gráfica

• Se menciona que herramientas como Claude pueden ayudar a interpretar gráficos mediante lectura de imágenes, facilitando así análisis visuales en investigaciones.

Ejemplo Práctico

Agricultural Preferences and Diversification

Overview of Crop Cultivation

• Six individuals are engaged in pea cultivation, while four focus on coffee, which is the least common crop with only one grower. This indicates a clear preference for traditional crops like potatoes and beans that are staples in local diets.

• Despite coffee being a commercially valuable product, its low presence suggests that farmers prefer to cultivate more essential food items. The diversification of crops implies that farmers aim to avoid dependency on a single product.

• A total of 33 individuals are involved in planting these crops. The discussion emphasizes the importance of graphical data representation for research purposes, highlighting how visual tools can enhance interpretation and understanding.

Practical Applications and Encouragement

• The speaker encourages participants to explore alternative methods and tools for agricultural practices, suggesting that innovation can lead to better outcomes beyond conventional approaches.