Foundations of Artificial Intelligence in Education

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• Foundations of Artificial Intelligence in Education

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• Vasuki, M
• Mishra, Anjay Kumar
• Dinesh Kumar, A
• Mishra, Shila
• Zulu, Lloyd
• Conteh, Daniel Kebbie

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• Artificial Intelligence (AI) has emerged as one of the most transformative technological innovations of the twenty-first century, fundamentally changing the way individuals learn, work, communicate, and solve complex problems. Although AI has become a prominent component of modern society only in recent decades, its conceptual foundations extend back several centuries through philosophical discussions concerning human intelligence, logical reasoning, and mechanical computation. The evolution of Artificial Intelligence represents a multidisciplinary journey involving computer science, mathematics, cognitive psychology, neuroscience, linguistics, philosophy, engineering, statistics, and data science. Continuous advancements in computational power, algorithmic development, data availability, and digital infrastructure have enabled AI to evolve from theoretical concepts into intelligent systems capable of performing tasks that traditionally required human intelligence. The earliest ideas related to artificial intelligence originated long before the invention of computers. Ancient civilizations imagined intelligent machines capable of mimicking human behaviour through myths, literature, and mechanical inventions. During the seventeenth and eighteenth centuries, philosophers and mathematicians such as René Descartes, Gottfried Wilhelm Leibniz, and George Boole contributed foundational concepts related to logic, reasoning, symbolic representation, and mathematical computation. Their work laid the intellectual groundwork for the development of computational systems capable of representing knowledge and making logical decisions. The modern evolution of Artificial Intelligence began during the twentieth century with the rapid advancement of computing technologies. In the 1930s and 1940s, significant progress in mathematical logic and computer science established the theoretical basis for intelligent machines. Alan Turing introduced the concept of a universal computing machine and later proposed the famous Turing Test, which examined whether a machine could demonstrate behaviour indistinguishable from that of a human being. Turing's pioneering contributions remain fundamental to contemporary AI research because they established that machines could potentially perform intelligent reasoning through computational processes. A major milestone occurred in 1956 during the Dartmouth Summer Research Project on Artificial Intelligence, organized by John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester. This conference formally introduced the term "Artificial Intelligence" and established AI as an independent scientific discipline. Researchers believed that human intelligence could be represented computationally and that machines could eventually perform reasoning, learning, language understanding, and problem-solving. The Dartmouth Conference inspired widespread academic interest and initiated decades of intensive research into intelligent computing systems. The period between the late 1950s and early 1970s is often referred to as the early development phase of Artificial Intelligence. During this era, researchers developed symbolic AI methods that emphasized logical reasoning, rule-based systems, theorem proving, and problem-solving algorithms. Early AI programmes successfully solved mathematical problems, played strategic games such as chess, and demonstrated limited natural language capabilities. Optimism surrounding AI research increased substantially as scientists believed that general human-level intelligence could be achieved within a relatively short period. However, limitations in computing power, data availability, memory capacity, and algorithmic efficiency restricted further progress. During the 1970s and 1980s, Artificial Intelligence experienced periods commonly known as the "AI Winters," characterized by reduced research funding, slower technological progress, and diminished public expectations. Many ambitious predi

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• 2026-09-30

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• https://doi.org/10.5281/zenodo.23059046

• Retrieved from Zenodo on 2026-10-09 in response to the search string “("artificial intelligence" OR "machine learning" OR "generative AI" OR "deep learning" OR "reinforcement learning" OR "large language model" OR "AI") AND ("AI concepts" OR "types of AI" OR "AI fundamentals" OR "recognizing AI" OR "recognising AI" OR "general versus narrow AI" OR "narrow AI" OR "general AI" OR "machine intelligence" OR "AI strengths and weaknesses" OR "traditional software" OR "rule-based systems" OR "introduction to AI" OR "introduction to artificial intelligence" OR "artificial intelligence introduction" OR "AI primer" OR "foundations of artificial intelligence" OR "overview of AI" OR "understanding AI" OR "history of AI" OR "AI essentials" OR "AI terminology" OR "metaphors for AI" OR "AI fundamental concepts" OR "AI key concepts" OR "philosophy of AI" OR "critical AI literacy")”. Zenodo served the resource and is not asserted to be its publisher or author.
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• URI: tag:aim-pro.eu,2026:oer/fba6588dfd1a
• URI: https://zenodo.org/records/23059047
• DOI: 10.5281/zenodo.23059047
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• Foundations of Artificial Intelligence in Education

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• Artificial Intelligence (AI) has emerged as one of the most transformative technological innovations of the twenty-first century, fundamentally changing the way individuals learn, work, communicate, and solve complex problems. Although AI has become a prominent component of modern society only in recent decades, its conceptual foundations extend back several centuries through philosophical discussions concerning human intelligence, logical reasoning, and mechanical computation. The evolution of Artificial Intelligence represents a multidisciplinary journey involving computer science, mathematics, cognitive psychology, neuroscience, linguistics, philosophy, engineering, statistics, and data science. Continuous advancements in computational power, algorithmic development, data availability, and digital infrastructure have enabled AI to evolve from theoretical concepts into intelligent systems capable of performing tasks that traditionally required human intelligence. The earliest ideas related to artificial intelligence originated long before the invention of computers. Ancient civilizations imagined intelligent machines capable of mimicking human behaviour through myths, literature, and mechanical inventions. During the seventeenth and eighteenth centuries, philosophers and mathematicians such as René Descartes, Gottfried Wilhelm Leibniz, and George Boole contributed foundational concepts related to logic, reasoning, symbolic representation, and mathematical computation. Their work laid the intellectual groundwork for the development of computational systems capable of representing knowledge and making logical decisions. The modern evolution of Artificial Intelligence began during the twentieth century with the rapid advancement of computing technologies. In the 1930s and 1940s, significant progress in mathematical logic and computer science established the theoretical basis for intelligent machines. Alan Turing introduced the concept of a universal computing machine and later proposed the famous Turing Test, which examined whether a machine could demonstrate behaviour indistinguishable from that of a human being. Turing's pioneering contributions remain fundamental to contemporary AI research because they established that machines could potentially perform intelligent reasoning through computational processes. A major milestone occurred in 1956 during the Dartmouth Summer Research Project on Artificial Intelligence, organized by John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester. This conference formally introduced the term "Artificial Intelligence" and established AI as an independent scientific discipline. Researchers believed that human intelligence could be represented computationally and that machines could eventually perform reasoning, learning, language understanding, and problem-solving. The Dartmouth Conference inspired widespread academic interest and initiated decades of intensive research into intelligent computing systems. The period between the late 1950s and early 1970s is often referred to as the early development phase of Artificial Intelligence. During this era, researchers developed symbolic AI methods that emphasized logical reasoning, rule-based systems, theorem proving, and problem-solving algorithms. Early AI programmes successfully solved mathematical problems, played strategic games such as chess, and demonstrated limited natural language capabilities. Optimism surrounding AI research increased substantially as scientists believed that general human-level intelligence could be achieved within a relatively short period. However, limitations in computing power, data availability, memory capacity, and algorithmic efficiency restricted further progress. During the 1970s and 1980s, Artificial Intelligence experienced periods commonly known as the "AI Winters," characterized by reduced research funding, slower technological progress, and diminished public expectations. Many ambitious predi

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• Vasuki, M (Srinivasan College of Arts and Science (Affiliated to Bharathidasan University), Perambalur, Tamil Nadu, India) [0009-0004-3085-9059] — author
• Mishra, Anjay Kumar (Dean, Madhesh University, Birgunj,Nepal) [0000-0003-2803-4918] — author
• Dinesh Kumar, A (Khadir Mohideen College (Affiliated to Bharathidasan University), Adirampattinam, Tamil Nadu, India) [0000-0001-6473-081X] — author
• Mishra, Shila (Visiting Faculty for M.Phill, Rajarshi Janak University, Janakpurdham, Nepal) [0000-0003-2731-351X] — author
• Zulu, Lloyd (World Christian University, Lusaka, Zambia) [0009-0008-2252-2420] — author
• Conteh, Daniel Kebbie (American Institute of Technology, Freetown, Sierra Leone) — author

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