Revolutionizing Education: The Opportunities and Challenges of AI
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Artificial intelligence (AI) is transforming various aspects of our society, from business and health care to entertainment and security. But what about education? How can AI enhance the quality and effectiveness of teaching and Learning? What are the benefits and risks of using AI in education? And what are the implications for educators, learners, policymakers, and society?
In this article, we will explore these questions by examining the role of AI in education, the opportunities it offers, and the challenges it poses. We will also provide recommendations for harnessing AI’s potential in education while addressing its limitations and pitfalls.
Table of Contents
Introduction
What is AI, and how does it work?
AI is a branch of computer science that aims to create machines or systems that can perform tasks that normally require human intelligence, such as reasoning, problem-solving, decision-making, Learning, perception, natural language processing, etc.
AI systems can be classified into two types: narrow AI and general AI. Narrow AI is a technique that can perform tasks within a limited domain, such as playing chess, recognizing faces, or translating languages. General AI refers to systems that can perform any intellectual task a human can do across multiple domains, such as understanding natural language, generating novel ideas, or exhibiting creativity. However, general AI is still a theoretical concept that has yet to be achieved.
Why is AI relevant for education?
AI is relevant for education because it can offer new ways of enhancing teaching and learning processes, outcomes, and experiences. AI can help personalize Learning according to each learner’s needs, preferences, abilities, goals, etc. AI can also help enhance teaching by automating tasks, such as grading or assessment, curriculum design or development, teacher professional development, etc. AI can also help improve access and equity in education by providing language translation or speech recognition services, assistive technologies for learners with special needs, open and online education platforms, etc.
However, AI is not a magic bullet that can solve all the problems or challenges in education. AI also poses ethical, social, technical, pedagogical, human, and cultural issues that must be addressed carefully. For example,
- How can we ensure the privacy and data protection of learners and educators who use AI systems?
- How can we avoid bias and discrimination in AI systems that may affect learning outcomes or opportunities?
- How can we ensure the accountability and transparency of AI systems that may influence learning decisions or behaviors?
- How can we guarantee the quality and reliability of AI systems that may affect learning quality or effectiveness?
- How can we integrate and interoperate AI systems with existing educational systems or practices?
- How can we align and assess the learning outcomes that AI systems may facilitate or foster?
- How can we balance the roles and skills of teachers and learners in AI-enhanced education?
- How can we preserve the social and emotional aspects of learning in AI-mediated education?
- How can we promote diversity and inclusion in AI education?
These are some of the questions we will explore in the following sections, where we will discuss the opportunities and challenges of AI in education.
Opportunities of AI in education
AI offers many opportunities for enhancing teaching and learning in various ways, such as personalizing Learning, enhancing education, and improving access and equity.
Personalized Learning
One of the main opportunities of AI in education is to provide personalized Learning for each learner. Personalized Learning refers to tailoring the learning content, pace, style, sequence, feedback, etc., according to each learner’s needs, preferences, abilities, goals, etc. Personalized Learning can help improve learner motivation, engagement, satisfaction, retention, performance, etc.
Some of the AI technologies that can facilitate personalized Learning are:
Adaptive learning systems
Adaptive learning systems can adapt the learning content or process based on the learner’s behavior, performance, feedback, etc. For example, an adaptive learning system can adjust the difficulty level, topic selection, question type, hint provision, etc., based on the learner’s progress, knowledge level, skill level, interest level, etc.
Some examples of adaptive learning systems are:
A web-based adaptive learning system that provides personalized instruction and assessment in mathematics and science subjects for K-12 and higher education students.
A platform that provides adaptive learning solutions for various issues and courses for K-12 and higher education students.
A platform that enables educators to create adaptive learning experiences for multiple subjects and courses for K-12 and higher education students.
Intelligent tutoring systems
Intelligent tutoring systems can provide personalized guidance or feedback to learners based on their actions or responses. For example, a smart tutoring system can provide hints, explanations, examples, scaffolding, etc., based on the learner’s errors, misconceptions, difficulties, etc.
Some examples of intelligent tutoring systems are:
- AutoTutor: A smart tutoring system that provides natural language dialogue and feedback to learners in various domains, such as physics, computer literacy, critical thinking, etc.
- Cognitive Tutor: An intelligent tutoring system that provides step-by-step guidance and feedback to learners in mathematics subjects for K-12 students.
- Betty’s Brain: A smart tutoring system that helps learners build causal models of science phenomena by providing metacognitive feedback and coaching.
Learning analytics and feedback
Learning analytics and feedback are processes or tools that collect, analyze, visualize, or provide data or information about learners’ activities, behaviors, performance, progress, etc. For example, learning analytics and feedback can provide dashboards, reports, recommendations, alerts, etc., based on learners’ data or information.
Some examples of learning analytics and feedback tools are:
- Learning Catalytics: A tool that enables instructors to create interactive questions and activities for learners and monitor their responses and performance in real time.
- Quizlet: A tool that allows learners to create flashcards and quizzes for various subjects and topics and track their progress and performance.
- Duolingo: A tool that allows learners to learn languages by providing adaptive exercises and feedback based on their level and goals.
Enhanced teaching
Another opportunity for AI in education is to enhance teaching by automating tasks or providing support for teachers. Enhanced teaching can help improve teacher efficiency, effectiveness, quality, etc.
Some of the AI technologies that can facilitate enhanced teaching are:
Automated grading and assessment
Automated grading and assessment are processes or tools that grade or assess learners’ work or performance automatically or semi-automatically. For example, automatic grading and assessment can provide scores, feedback, rubrics, etc., based on learners’ answers, essays, projects, etc.
Some examples of automated grading and assessment tools are:
- Gradescope: A tool that enables instructors to grade and provide feedback for various assignments, such as multiple-choice, short-answer, programming, etc.
- Turnitin: A tool that allows instructors to to check and prevent plagiarism in learners’ essays, papers, etc.
- Edulastic: A tool that allows instructors to create and administer online assessments for various subjects and standards for K-12 students.
Curriculum design and development
Curriculum design and development are processes or tools to design or develop curricula or learning materials for various subjects or courses. For example, curriculum design and development can provide objectives, outcomes, standards, activities, resources, etc., based on learners’ needs, preferences, abilities, goals, etc.
Some examples of curriculum design and development tools are:
- OER Commons: A platform that enables educators to find, create, and share open educational resources for various subjects and levels.
- CourseBuilder: A platform that allows educators to create online courses using various tools and templates.
- AI Curriculum: A platform that provides a comprehensive and interactive curriculum for teaching and learning AI for K-12 students.
Teacher professional development
Teacher professional developments are processes or tools that can provide training or support for teachers to improve their knowledge, skills, attitudes, etc., related to teaching and Learning. For example, teacher professional development can provide courses, workshops, mentoring, coaching, feedback, etc., based on teachers’ needs, interests, goals, etc.
Some examples of teacher professional development tools are:
- Coursera: A platform that provides online courses and certificates for various topics and domains, including education and teaching.
- Teachable Machine: A tool that enables teachers to learn how to create and use AI models without coding.
- Teach to One: A tool that provides personalized coaching and feedback for teachers who use adaptive learning systems for mathematics instruction.
Improved access and equity
Another opportunity for AI in education is to improve access and equity in education by providing services or solutions that can overcome some barriers or challenges that learners or educators may face. Improved access and equity can help increase learner participation, inclusion, diversity, opportunity, etc.
Some of the AI technologies that can facilitate improved access and equity are:
Language translation and speech recognition
Language translation and speech recognition are processes or tools that can translate or recognize languages or speech for learners or educators who speak different languages or have communication difficulties. For example, language translation and speech recognition can provide subtitles, captions, transcripts, etc., based on learners’ or educators’ speech or text.
Some examples of language translation and speech recognition tools are:
- Google Translate: A tool that enables learners or educators to translate text or speech between various languages.
- Microsoft Translator: A tool that allows learners or educators to translate text or speech between various languages and also provides a live presentation feature that can translate speech and display subtitles in real time.
- Otter.ai: A tool that enables learners or educators to transcribe and share audio recordings or live conversations.
Assistive technologies for learners with special needs
Assistive technologies for learners with special needs are devices or software that can assist learners with physical, cognitive, emotional, or behavioral disabilities or difficulties. For example, assistive technologies for learners with special needs can provide text-to-speech, speech-to-text, screen readers, magnifiers, etc., based on learners’ needs or preferences.
Some examples of assistive technologies for learners with special needs are:
- Read&Write: A tool that provides various features to help learners with reading, writing, studying, etc., such as text-to-speech, speech-to-text, word prediction, dictionary, highlighter, etc.
- G Suite for Education: A suite of tools that provides various accessibility features for learners with special needs, such as voice typing, screen reader, braille support, closed captions, etc.
- Proloquo2Go: A tool that provides a symbol-based communication system for learners with difficulty speaking.
Open and online education platforms
Open and online education platforms provide free or low-cost access to various educational resources or opportunities for learners or educators with limited access to formal education or face geographical, financial, sociocultural, or other constraints. For example, open and online education platforms can provide courses, certificates, degrees, badges, etc., based on learners’ interests, goals, levels, etc.
Some examples of open and online education platforms are:
- edX: A platform that provides free online courses and certificates from various universities and organizations worldwide.
- Khan Academy: A platform that offers free online lessons and exercises for various subjects and levels for K-12 students.
- Udemy: A platform that offers low-cost online courses on various topics and domains for lifelong learners.
Challenges of AI in education
AI also poses some challenges to education that need to be addressed carefully. These challenges can be classified into three categories: ethical and social issues, technical and pedagogical issues, and human and cultural issues.
Ethical and social issues
Ethical and social issues are issues that relate to the values, norms, rights, responsibilities, etc., of learners, educators, policymakers, and society at large who are involved in or affected by AI in education. Some of the ethical and social issues that AI in education may raise are:
Privacy and data protection
Privacy and data protection are issues that relate to the collection, storage, use, or sharing of personal or sensitive data or information of learners or educators who use AI systems. For example, privacy and data protection issues may arise when AI systems collect or use learners’ or educators’ data or information without their consent or knowledge or when AI systems share or disclose their data or information with third parties without their permission or control.
Some of the possible consequences of privacy and data protection issues are:
- Loss of trust or confidence in AI systems or providers
- Loss of autonomy or agency over personal or sensitive data or information
- Loss of identity or dignity due to exposure or misuse of personal or sensitive data or information
- Loss of security or safety due to theft or hacking of personal or sensitive data or information
Some of the possible solutions for privacy and data protection issues are:
- Adopting ethical principles or guidelines for AI in education
- Implementing legal regulations or policies for AI in education
- Developing technical standards or protocols for AI in education
- Educating learners or educators about their rights and responsibilities regarding their data or information
- Empowering learners or educators to control their data or information
Bias and discrimination
Bias and discrimination are issues that relate to the fairness, equality, justice, etc., of learners or educators who use AI systems. For example, bias and discrimination issues may arise when AI systems produce or reproduce unfair or unequal outcomes or opportunities for learners or educators based on their characteristics, such as gender, race, ethnicity, religion, socioeconomic status, etc.
Some of the possible consequences of bias and discrimination issues are:
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of access or equity in learning opportunities or resources
- Loss of diversity or inclusion in learning communities or environments
- Loss of respect or recognition for learners’ or educators’ identities or cultures
Some of the possible solutions for bias and discrimination issues are:
- Adopting ethical principles or guidelines for AI in education
- Implementing legal regulations or policies for AI in education
- Developing technical standards or protocols for AI in education
- Educating learners or educators about the sources and impacts of bias and discrimination in AI systems
- Empowering learners or educators to challenge or report prejudice and discrimination in AI systems
Accountability and transparency
Accountability and transparency are issues that relate to the responsibility, explainability, verifiability, etc., of learners, educators, policymakers, and society at large who are involved in or affected by AI in education. For example, accountability and transparency issues may arise when AI systems influence or affect learning decisions or behaviors without providing clear or accurate reasons or evidence or when AI systems operate or function without being monitored or evaluated.
Some of the possible consequences of accountability and transparency issues are:
- Loss of trust or confidence in AI systems or providers
- Loss of autonomy or agency over learning decisions or behaviors
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of security or safety due to errors or failures of AI systems
Some of the possible solutions for accountability and transparency issues are:
- Adopting ethical principles or guidelines for AI in education
- Implementing legal regulations or policies for AI in education
- Developing technical standards or protocols for AI in education
- Educating learners or educators about the mechanisms and impacts of AI systems
- Empowering learners or educators to question or verify AI systems
Technical and pedagogical issues
Technical and pedagogical issues are issues that relate to the quality, reliability, integration, interoperability, alignment, assessment, etc., of AI systems or solutions in education. Some of the technical and pedagogical issues that AI in education may face are:
Quality and reliability of AI systems
AI systems’ Quality and reliability are issues related to the accuracy, validity, consistency, robustness, etc., of AI systems or solutions in education. For example, problems of quality and reliability of AI systems may arise when AI systems produce incorrect, invalid, inconsistent, or unreliable results or outputs for learners or educators.
Some of the possible consequences of quality and reliability of AI systems issues are:
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of trust or confidence in AI systems or providers
- Loss of security or safety due to errors or failures of AI systems
- Loss of accountability or transparency due to lack of evidence or verification of AI systems
Some of the possible solutions for quality and reliability of AI systems issues are:
- Developing technical standards or protocols for AI in education
- Implementing quality assurance or evaluation mechanisms for AI in education
- Educating learners or educators about the limitations and uncertainties of AI systems
- Empowering learners or educators to provide feedback or report errors or failures of AI systems
Integration and interoperability of AI systems
Integration and interoperability of AI systems are issues that relate to the compatibility, coordination, communication, etc., of AI systems or solutions with existing educational systems or practices. For example, problems with the integration and interoperability of AI systems may arise when AI systems do not work or interact well with other educational technologies, tools, resources, etc., or when AI systems do not align or match existing educational curricula, standards, policies, etc.
Some of the possible consequences of integration and interoperability of AI systems issues are:
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of access or equity in learning opportunities or resources
- Loss of diversity or inclusion in learning communities or environments
- Failure of accountability or transparency due to lack of coordination or communication between AI systems
Some of the possible solutions for integration and interoperability of AI systems issues are:
- Developing technical standards or protocols for AI in education
- Implementing integration or interoperability mechanisms for AI in education
- Educating learners or educators about the benefits and challenges of integrating or interoperating AI systems with existing educational systems or practices
- Empowering learners or educators to customize or adapt AI systems to their educational contexts or needs
Alignment and assessment of learning outcomes
Alignment and assessment of learning outcomes are issues that relate to the relevance, appropriateness, measurability, etc., of the learning outcomes that AI systems or solutions may facilitate or foster in education. For example, alignment and assessment of learning outcomes issues may arise when AI systems do not support or reflect the intended learning outcomes for learners or educators or when AI systems do not provide valid, reliable, or meaningful measures or indicators of learning outcomes for learners or educators.
Some of the possible consequences of alignment and assessment of learning outcomes issues are:
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of trust or confidence in AI systems or providers
- Loss of accountability or transparency due to lack of evidence or verification of learning outcomes facilitated or fostered by AI systems
Some of the possible solutions for alignment and assessment of learning outcomes issues are:
- Developing technical standards or protocols for AI in education
- Implementing alignment and assessment mechanisms for AI in education
- Educating learners or educators about the intended and actual learning outcomes facilitated or fostered by AI systems.
- Empowering learners or educators to provide feedback or report discrepancies or gaps between intended and actual learning outcomes facilitated or fostered by AI systems
Human and cultural issues
Human and cultural issues are issues that relate to the roles, skills, attitudes, emotions, values, beliefs, etc., of learners, educators, policymakers, and society at large who are involved in or affected by AI in education. Some of the human and cultural issues that AI in education may entail are:
Teacher and learner roles and skills
Teacher and learner roles and skills are issues that relate to the expectations, responsibilities, competencies, etc., of teachers and learners who use AI systems or solutions in education. For example, teacher and learner roles and skills issues may arise when AI systems change or challenge the traditional roles or skills of teachers or learners, such as providing instruction, guidance, feedback, etc., or requiring new or different skills, such as critical thinking, creativity, collaboration, etc.
Some of the possible consequences of teacher and learner roles and skills issues are:
- Loss of quality or effectiveness of teaching or learning outcomes or processes
- Loss of trust or confidence in teachers or learners
- Loss of identity or dignity of teachers or learners
- Loss of motivation or engagement of teachers or learners
Some of the possible solutions for teacher and learner roles and skills issues are:
- Adopting ethical principles or guidelines for AI in education
- Implementing legal regulations or policies for AI in education
- Developing technical standards or protocols for AI in education
- Educating teachers or learners about the changing or emerging roles or skills of teachers or learners in AI-enhanced education
- Empowering teachers or learners to develop or enhance their roles or skills in AI-enhanced education
Social and emotional aspects of Learning
Social and emotional aspects of Learning are issues that relate to the feelings, emotions, relationships, values, beliefs, etc., of learners or educators who use AI systems or solutions in education. For example, social and emotional aspects of learning issues may arise when AI systems affect or neglect the social or emotional needs or experiences of learners or educators, such as providing social interaction, support, recognition, etc., or fostering social or emotional skills, such as empathy, self-regulation, resilience, etc.
Some of the possible consequences of social and emotional aspects of learning issues are:
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of trust or confidence in AI systems or providers
- Loss of identity or dignity of learners or educators
- Loss of motivation or engagement of learners or educators
Some of the possible solutions for social and emotional aspects of learning issues are:
- Adopting ethical principles or guidelines for AI in education
- Implementing legal regulations or policies for AI in education
- Developing technical standards or protocols for AI in education
- Educating learners or educators about the social or emotional impacts or benefits of AI systems
- Empowering learners or educators to express or address their social or emotional needs or experiences in AI-mediated education
Diversity and Inclusion in AI Education
Diversity and inclusion in AI education are issues that relate to the representation, participation, recognition, etc., of various groups or individuals involved in or affected by AI in education. For example, diversity and inclusion in AI education issues may arise when AI systems do not reflect or respect the diversity or inclusion of learners or educators based on their characteristics, such as gender, race, ethnicity, religion, socioeconomic status, etc., or when AI systems do not provide equal or equitable opportunities or resources for learners or educators based on their characteristics.
Some of the possible consequences of diversity and inclusion in AI education issues are:
- Loss of quality or effectiveness of learning outcomes or processes
- Loss of access or equity in learning opportunities or resources
- Loss of diversity or inclusion in learning communities or environments
- Loss of respect or recognition for learners’ or educators’ identities or cultures
Some of the possible solutions for diversity and inclusion in AI education issues are:
- Adopting ethical principles or guidelines for AI in education
- Implementing legal regulations or policies for AI in education
- Developing technical standards or protocols for AI in education
- Educating learners or educators about the importance and challenges of diversity and inclusion in AI education
- Empowering learners or educators to promote or advocate for diversity and inclusion in AI education
Conclusion
AI is a powerful and promising technology that can offer many opportunities for enhancing teaching and learning in various ways. However, AI also poses challenges that must be addressed carefully and responsibly. Therefore, it is important to adopt a balanced and holistic approach to AI in education that considers the technical and pedagogical aspects and the ethical, social, human, and cultural aspects of AI in education. By doing so, we can harness the potential of AI in education while minimizing its risks and pitfalls.
Here are some of the key takeaways from this article:
- AI can provide personalized Learning for each learner by adapting the learning content or process based on the learner’s behavior, performance, feedback, etc.
- AI can also enhance teaching by automating some tasks or providing support for teachers, such as grading or assessment, curriculum design or development, teacher professional development, etc.
- AI can also improve access and equity in education by providing services or solutions that can overcome some barriers or challenges that learners or educators may face, such as language translation or speech recognition, assistive technologies for learners with special needs, open and online education platforms, etc.
- However, AI raises some ethical and social issues related to the values, norms, rights, responsibilities, etc., of learners, educators, policymakers, and society involved in or affected by AI in education. These issues include privacy and data protection, bias and discrimination, accountability and transparency, etc.
- AI also faces technical and pedagogical issues related to the quality, reliability, integration, interoperability, alignment, assessment, etc., of AI systems or solutions in education. Some of these issues are the quality and reliability of AI systems, integration and interoperability of AI systems, alignment and assessment of learning outcomes, etc.
- AI also entails some human and cultural issues related to the roles, skills, attitudes, emotions, values, beliefs, etc., of learners, educators, policymakers, and society involved in or affected by AI in education. These issues include teacher and learner roles and skills, social and emotional aspects of Learning, diversity, and inclusion in AI education, etc.
- Therefore, it is important to adopt a balanced and holistic approach to AI in education that considers the technical and pedagogical aspects and the ethical, social, human, and cultural aspects of AI in education. By doing so, we can harness the potential of AI in education while minimizing its risks and pitfalls.
FAQs
Q: What is AI, and how does it work?
A: AI is a branch of computer science that aims to create machines or systems that can perform tasks that normally require human intelligence, such as reasoning, problem-solving, decision-making, Learning, perception, natural language processing, etc. AI systems can work using various methods or techniques, such as machine learning, deep Learning, natural language processing, computer vision, etc.
Q: What are the benefits of using AI in education?
A: Some of the benefits of using AI in education are:
- It can provide personalized Learning for each learner according to their needs, preferences, abilities, goals, etc.
- It can enhance teaching by automating some tasks or providing support for teachers, such as grading or assessment, curriculum design or development, teacher professional development, etc.
- It can improve access and equity in education by providing services or solutions that can overcome some barriers or challenges that learners or educators may face, such as language translation or speech recognition, assistive technologies for learners with special needs, open and online education platforms, etc.
Q: What are the risks or challenges of using AI in education?
A: Some of the risks or challenges of using AI in education are:
- It may raise some ethical and social issues related to the values, norms, rights, responsibilities, etc., of learners, educators, policymakers, and society involved in or affected by AI in education. These issues include privacy and data protection, bias and discrimination, accountability and transparency, etc.
- It may face technical and pedagogical issues related to the quality, reliability, integration, interoperability, alignment, assessment, etc., of AI systems or solutions in education. Some of these issues are the quality and reliability of AI systems, integration and interoperability of AI systems, alignment and assessment of learning outcomes, etc.
- It may entail some human and cultural issues related to the roles, skills, attitudes, emotions, values, beliefs, etc., of learners, educators, policymakers, and society involved in or affected by AI in education. Some of these issues are teacher and learner roles and skills.
Q: How can we address the risks or challenges of using AI in education?
A: Some of the possible ways to address the risks or challenges of using AI in education are:
- Adopting ethical principles or guidelines for AI in education can provide a common framework or reference for learners, educators, policymakers, and society involved in or affected by AI in education.
- It is implementing legal regulations or policies for AI in education that can provide a legal basis or protection for learners, educators, policymakers, and society at large who are involved in or affected by AI in education.
- It develops technical standards or protocols for AI in education to ensure the quality, reliability, integration, interoperability, alignment, assessment, etc., of AI systems or solutions.
- Educating learners or educators about the benefits and challenges of AI in education can increase their awareness, understanding, and critical thinking about AI in education.
- Empowering learners or educators to control, customize, adapt, question, verify, challenge, report, etc., their use or experience of AI systems or solutions in education.
Q: What are some examples of AI technologies or tools that can be used in education?
A: Some of the examples of AI technologies or tools that can be used in education are:
- Adaptive learning systems that can adapt the learning content or process based on the learner’s behavior, performance, feedback, etc., such as ALEKS, Knewton, Smart Sparrow, etc.
- Intelligent tutoring systems that can provide personalized guidance or feedback to learners based on their actions or responses, such as AutoTutor, Cognitive Tutor, Betty’s Brain, etc.
- I am learning analytics and feedback tools that can collect, analyze, visualize, or provide data or information about learners’ activities, behaviors, performance, progress, etc., such as Learning Catalytics, Quizlet, Duolingo, etc.
- It has automated grading and assessment tools that can grade or assess learners’ work or performance automatically or semi-automatically, such as Gradescope, Turnitin, Edulastic, etc.
- Curriculum design and development tools that can design or develop curriculum or learning materials for various subjects or courses, such as OER Commons, CourseBuilder, AI Curriculum, etc.
- Teacher professional development tools that can provide training or support for teachers to improve their knowledge, skills, attitudes, etc., related to teaching and Learning, such as Coursera, Teachable Machine, Teach to One, etc.
- Language translation and speech recognition tools that can translate or recognize languages or speech for learners or educators who speak different languages or have communication difficulties, such as Google Translate, Microsoft Translator, Otter.ai, etc.
- Assistive technologies for learners with special needs that can assist learners who have physical, cognitive, emotional, or behavioral disabilities or difficulties, such as Read&Write, G Suite for Education, Proloquo2Go, etc.
- Open and online education platforms can provide free or low-cost access to various educational resources or opportunities for learners or educators with limited access to formal education or face geographical, financial, sociocultural, or other constraints, such as edX, Khan Academy, Udemy, etc.
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