Did you know that artificial intelligence and contextual learning can help improve the quality, effectiveness, and utility of your education? In this article, you’ll find answers to several questions: What is artificial intelligence and contextual learning, and how are they applied in education? What are the benefits, challenges, and risks of using these technologies in schools? What are the best practices and examples of using these technologies in educational settings?

What is contextual learning? Contextual learning is a type of education where students use real-life situations and problems to apply and develop their knowledge and skills. This allows them to see the purpose and relevance of what they are learning and adapt to different situations. Contextual learning is based on the idea that learning is more effective when connected to students’ lives and interests.

Contextual learning was not invented by a single person or in a single year; it has been developed over the years by various educational theorists and practitioners. Some of the most well-known names associated with contextual learning include:

1. John Dewey (1859 - 1952): An American philosopher and educator who believed that education should be based on students’ experiences, interests, and needs rather than abstract facts and rules.
2. Jean Piaget (1896 - 1980): A Swiss psychologist and epistemologist who studied how children develop their knowledge through interaction with the environment and problem-solving.
3. Lev Vygotsky (1896 - 1934): A Soviet psychologist and educator who emphasized the role of social context, language, and culture in learning and students’ development.
4. Jerome Bruner (1915 - 2016): An American psychologist and educator who introduced the idea of a spiral curriculum, presenting educational content at different levels of complexity and significance for students.
5. David Kolb (born 1939): An American psychologist and educator who developed an experiential learning model describing four stages of learning: concrete experience, reflective observation, abstract conceptualization, and active experimentation.

Contextual learning can be traced back to the early 20th century when Dewey advocated for learning through action rather than passive memorization. In the 1970s and 1980s, contextual learning gained popularity among scholars and teachers seeking alternatives to traditional education. In the 1990s, contextual learning expanded to incorporate ideas from cognitive science, constructivism, situated learning, and other theories. Today, contextual learning is applied in various fields and disciplines, including mathematics, languages, science, art, technology, and more.

The main principles of contextual learning are:

• Principle of scientific basis** - learning should be based on strictly scientific knowledge, facts, principles, ideas, and regularities proven in practice.
• Principle of consciousness - learning should relate to students’ understanding and attitude towards the educational process, increasing their motivation, interest, and positive emotions.
• Principle of activity - learning should stimulate active and creative thinking, action, and participation of students, developing their applied and transferable skills.
• Principle of visualization - learning should use various visual aids that facilitate, support, and make teaching more attractive, creating a comprehensive view, enriching sensory experience, and developing students’ observational skills.
• Principle of accessibility - learning should be tailored to the capabilities, age, level of development, and stage of education of the students, following the rules from easy to difficult, from known to unknown, from simple to complex, from near to far, from concrete to abstract.
• Principle of systematicity and sequence - learning should present educational content in a strict order, not be premature, be logically connected in a coherent system, be consistent with the experience and level of the learners, ensuring continuity in education and connection between different disciplines.
• Principle of individual and differentiated approach - learning should take into account individual differences, specific needs, interests, and preferences of students, offering various forms, methods, and means of education, allowing for self-expression and self-assessment. Contextual learning follows these principles to make learning more effective, meaningful, and useful for students.

Some examples of contextual learning are:

• Studying history through visits to museums, monuments, or historical sites.
• Studying mathematics through solving problems related to budgets, charts, statistics, or geometry.
• Studying languages through communication with native speakers, reading authentic texts, or watching movies.
• Studying science through conducting experiments, observing natural phenomena, or researching current issues. Contextual learning helps students develop critical thinking, creativity, collaboration, and independence. It also prepares them for their future careers and civic responsibility.

Some of the advantages of contextual learning are:

• Increases motivation and interest of students in learning because they see the connection between what they learn and what they need in life.
• Develops applied and transferable skills of students, as they can use their knowledge and skills in various situations and contexts.
• Improves memory and retention of students because they actively engage with the material and connect it to previous knowledge and experience.
• Stimulates creativity and innovation in students as they encounter real-world problems and need to find original and effective solutions.
• Supports social and emotional development of students as they work in groups, share ideas, express opinions, and respect differences.

Some examples of contextual learning include:

• Studying geography through the use of augmented reality, allowing students to see and explore different places and phenomena on the world map.
• Studying biology through virtual experiments that demonstrate how various organs and systems work in the human body.
• Studying literature by creating their own stories that reflect personal experiences and viewpoints.

Contextual learning helps students develop critical thinking, creativity, collaboration, and independence. It also prepares them for their future careers and civic responsibility.

To implement contextual learning in the educational process, we need to follow several steps:

1. Analyze students’ needs, interests, and preferences to determine suitable topics, goals, and learning tasks.
2. Select or create real-life situations and problems that challenge students to use and develop their knowledge and skills.
3. Provide students access to diverse information sources, including textbooks, the internet, media, experts, peers, etc.
4. Organize interactive and collaborative work, such as group discussions, projects, presentations, games, simulations, etc.
5. Evaluate the process and results of learning, providing feedback, encouragement, and support to students.

While contextual learning is an effective and modern teaching method, it is not without its limitations. Some of these include:

• Requires more time, resources, and preparation from teachers to create and conduct quality and engaging contextual tasks and situations.
• Assessing effectiveness and student progress can be challenging due to the dynamic, diverse, and non-standard nature of contextual learning.
• May lead to loss of focus or confusion if the context is too complex, unclear, or unrelated to the learning goal.
• May face resistance or dissatisfaction from students or parents accustomed to traditional approaches and who do not see the benefits of contextual learning.

Contextual learning has its challenges and limitations, but when applied properly and reasonably, it can be highly beneficial and enjoyable for students.

An ideal ally to contextual learning is artificial intelligence.

Artificial Intelligence 

Artificial Intelligence (AI) is a term coined by John McCarthy, an American mathematician, programmer, and linguist, in 1955 during the Dartmouth Summer Research Project on Artificial Intelligence. This project is considered the beginning of AI research, bringing together a group of scientists interested in creating machines capable of performing intelligent tasks.

AI is the science behind the concepts that enable computers to perform tasks that can be considered intelligent, such as reasoning, learning, perception, planning, communication, and more. AI has numerous applications across various fields, including web search, recommendation systems, speech recognition, self-driving cars, and more.

How has artificial intelligence evolved from 1955 to today? AI has undergone significant changes and progress since 1955 when John McCarthy coined the term. Here are some key stages in the development of AI:

1. 1950-1970: The Golden Age of AI
   • First programs demonstrating AI elements emerge, including chess-playing, speech recognition, language translation, and logical problem-solving.
   • Theoretical foundations of AI are developed, including the Turing test, Gödel’s theorem, information theory, and more.
2. 1970-1980: The AI Winter
   • Interest and funding in AI decline due to unrealistic expectations, technical challenges, lack of standards, and competition from other fields.
   • Doubts and criticisms arise regarding the capabilities and ethics of AI.
3. 1980-1990: The AI Renaissance
   • Interest and funding in AI revive with new approaches and applications, such as expert systems, neural networks, genetic algorithms, and more.
   • New areas emerge, including robotics, computer vision, natural language processing, and more.
4. 1990-2010: The AI Boom
   • Significant progress and expansion of AI occur due to the internet, hardware advancements, software development, data availability, and improved algorithms.
   • AI becomes more practical, accessible, and influential in various aspects of life and business.
   • Concepts like machine learning gain prominence.
5. 2010-Present: The AI Explosion
   • Tremendous growth and diversification of AI, fueled by big data, powerful computers, cloud services, open-source code, and innovative models.
   • AI reaches new levels of performance, creativity, and intelligence in areas like web search, recommendation systems, speech recognition, self-driving cars, and more.
   • New challenges arise related to regulation, ethics, security, and the future of AI.

AI remains one of the most dynamic and progressive fields in science and technology, continually evolving and improving. 

Artificial Intelligence (AI) can assist in implementing contextual learning in various ways. Some of them include:

1. Personalizing Learning:
   • AI can adapt education based on individual students’ needs, interests, and preferences by offering suitable tasks, resources, and feedback.
2. Supporting Teachers:
   • AI can aid teachers in creating and conducting contextual tasks and situations by providing tools for content generation, analysis, and assessment.
3. Enriching Contextual Learning:
   • AI has the potential to enhance contextual learning with innovative and interactive technologies such as virtual and augmented reality, robotics, games, and simulations.
4. Improving Assessment Efficiency:
   • AI can improve the quality and reduce the time required for assessment by using automated and adaptive systems for formative and summative evaluation.
5. Enhancing Effectiveness and Engagement:
   • AI has the capacity to make contextual learning more effective, attractive, and enjoyable for both students and teachers.

How can we apply AI in contextual learning? To apply AI in contextual learning, we can use various tools and technologies based on AI or utilizing AI to enhance education. Some examples include:

1. AI Applications for Students:
   • AI-powered applications can help students learn various subjects such as mathematics, languages, science, art, etc. These applications adapt content, pace, and difficulty levels according to individual student needs. They provide feedback, support, generate tests, games, and interactions, and recognize voice, text, and images.
2. AI Platforms for Teachers:
   • Platforms supporting teachers in creating and conducting contextual tasks and situations offer tools for content generation, analysis, and student assessment. These platforms allow personalization of learning, tracking student progress, communication, and collaboration with students and colleagues.
3. AI Technologies for Enrichment:
   • Innovative technologies like virtual and augmented reality, robotics, games, and simulations can enrich contextual learning. They enable students to experience and explore different situations, engage in practical and creative activities, and learn more effectively.
4. Customizing AI Implementation:
   • The choice of AI implementation depends on educational goals, teacher and student resources, and student interests and preferences. AI has the potential to enhance quality, effectiveness, and attractiveness in contextual learning.

What is valuable when applying artificial intelligence and contextual learning in schools? Implementing artificial intelligence (AI) and contextual learning (CL) in schools can bring valuable benefits to students, teachers, and the educational system. Together, AI and CL can prepare students for the future by developing essential skills such as critical thinking, problem-solving, collaboration, communication, digital literacy, and more

What are the best practices for implementing AI and CL in schools? To implement AI and CL in schools, teachers and students can follow some best practices, such as:

• Choosing suitable and interesting topics, goals, and tasks for learning that are related to real life and the needs of the students.
• Using a variety of accessible information sources and technologies, such as AI-based chatbots, virtual and augmented reality, 3D printing, games, and simulations.
• Organizing interactive and collaborative forms of work, such as group discussions, projects, presentations, experiments, etc.
• Evaluating the process and results of learning, using AI for formative and summative assessment, providing feedback, encouragement, and support to students.
• Developing critical thinking, creativity, collaboration, and other important skills necessary for the future.
• Monitoring the ethical, security, and regulatory aspects of using AI and CL, respecting the rights, dignity, and differences of students. AI and CL are two of the most promising and innovative areas of education, which, when used together, contribute to increasing the effectiveness, attractiveness, and usefulness of learning.

What are the main challenges and risks of using AI and CL in education? The use of AI and CL in education can bring many benefits but also create some challenges and risks that need to be considered. Some of them are:

• Lack of qualified personnel - to effectively use AI and CL, teachers and students who understand the basics of these technologies, have sufficient digital skills, and can adapt to the changing environment are needed. According to a study by the European Commission, only 20% of teachers in the EU are confident in their digital skills, and 70% of students do not receive enough training in programming and robotics.
• Violation of personal data and privacy - the use of AI and CL requires the collection, processing, and storage of large volumes of personal data of students and teachers, which can be exposed to misuse, theft, hacker attacks, or criminal use. To prevent this, strict data protection rules must be followed, consent from stakeholders must be obtained, and cybersecurity measures must be applied.
• Discrimination and bias - the use of AI and CL can lead to unequal treatment of students and teachers if AI systems are developed or trained with incomplete, inaccurate, or biased data, algorithms, or models. This can negatively impact the assessment, ranking, access to education, and career development of students and teachers, especially those from vulnerable or minority groups. To avoid this, transparency, explainability, and fairness of AI systems must be ensured, regular checks and impact assessments on fundamental rights must be conducted, and diverse voices and perspectives must be included in the development and implementation process of AI and CL.
• Opacity and accountability - the use of AI and CL can lead to a loss of control, understanding, and trust by students and teachers if AI systems are complex, hidden, or inaccessible for inspection and feedback. This can have negative consequences for the quality, effectiveness, and usefulness of education, as well as for the rights, freedoms, and interests of students and teachers. To prevent this, accountability, participation, and dialogue among all stakeholders must be ensured, ethical principles and standards must be followed, and mechanisms for complaints and sanctions for violations of fundamental rights must be established. The use of AI and CL in education requires a balance between the opportunities and risks that these technologies bring. To use them in a more responsible and sustainable way, the rules and principles for the protection of fundamental rights must be followed, the needs and expectations of students and teachers must be considered, and conditions for collaboration and innovation among all participants in the educational process must be created.

Conclusion The use of contextual learning and artificial intelligence in schools is a contemporary and innovative approach that can improve the quality, effectiveness, and usefulness of education. Contextual learning focuses on connecting educational content with real life and the needs of students, using various types of context, such as professional, life, and technological. Artificial intelligence is used to support teachers and students by providing tools for creating, analyzing, evaluating, and feedback on educational content, personalizing, adapting, and interactivity of the learning process, and developing important skills and competencies for the future. The use of contextual learning and artificial intelligence in schools requires a balance between the opportunities and risks that these technologies bring, following the rules and principles for the protection of fundamental rights, considering the needs and expectations of students and teachers, and creating conditions for collaboration and innovation among all participants in the educational process.

Source: cpocreativity.com