Foundations of Artificial Intelligence in Education
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 hum…
Licence
OPEN
CC-BY-4.0
Authors
Vasuki, M, Mishra, Anjay Kumar, Dinesh Kumar, A, Mishra, Shila, Zulu,…
Built from what the sources declared and what the gates observed.
Nothing absent has been filled in here.
2 values read out of the text by the enrichment rules stand under their elements, marked inferred, and are kept apart in the exports.
DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection.
the standard ↗
The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.
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
The abstract, and the depositor's additional notes after it when the source has a field for them.
Subjectdcterms:subject
none found — the source did not declare it
Declared keywords and taxonomy terms, each carrying its scheme.
Languagedcterms:language
language gate
read a heuristic
en
The language of the content as the language gate read it.
Audiencedcterms:audience
none found — the source did not declare it
Imported when the source declares an intended audience. Never inferred.
Education leveldcterms:educationLevel
none found — the source did not declare it
Imported when the source declares an educational context.
Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.
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.
Text extracted from pdf to Markdown by pdf-inspector; the original is retained unchanged beside it.
Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.
IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other.
the standard ↗
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
The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.
Keyword1.5
none found — the source did not declare it
Coverage1.6
not collected — this library does not fill it
The time, place or culture the resource applies to. No source declares it.
Structure1.7
not collected — this library does not fill it
Not declared by any of the three sources, and not estimated here.
Aggregation level1.8
not collected — this library does not fill it
Not declared by any of the three sources, and not estimated here.
2 Life cycle
1/3
Version2.1
none found — the source did not declare it
Status2.2
none found — the source did not declare it
Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.
Vasuki, M (Srinivasan College of Arts and Science (Affiliated to Bharathidasan University), Perambalur, Tamil Nadu, India) [0009-0004-3085-9059] — author
Zulu, Lloyd (World Christian University, Lusaka, Zambia) [0009-0008-2252-2420] — author
Conteh, Daniel Kebbie (American Institute of Technology, Freetown, Sierra Leone) — author
Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.
3 Meta-metadata
4/4
Identifier3.1
this engine
resource_id
URI: tag:aim-pro.eu,2026:oer/fba6588dfd1a/record
The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute3.2
this engine
source
AIM-PRO WP3 OER harvester (Zenodo) — creator
Who generated this record and when — a statement about the record, not about the resource.
Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
Markdown extracted from the original by pdf-inspector
The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.
8 Annotation
0/3
Entity8.1
not collected — this library does not fill it
Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.
Date8.2
not collected — this library does not fill it
Description8.3
not collected — this library does not fill it
9 Classification
0/4
Purpose9.1
not collected — this library does not fill it
Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.
Taxon path9.2
not collected — this library does not fill it
Where the competency framework goes. Empty in the record for the reason above.
Description9.3
not collected — this library does not fill it
Keyword9.4
not collected — this library does not fill it
What could not be established6
Where the source's metadata could not be carried
over as it was — missing, contradictory, with no matching term in the
standard, restructured, or taken from the repository — and what was done
instead. Without these notes, an empty element would look like something
the harvester missed.
Status
Field
Why
Not available
subjects
the source published no keywords
Not available
declared_language
the source declared no language; the recorded content language is this engine's reading of the text
Not available
rights_holder
no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available
publisher
the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available
educational
the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them
Since read out of the text by the enrichment rules: the learning resource type (5.2), shown above under its element, marked inferred.
Declared, not mapped
declared_type
the declared type 'Book chapter' has no justified LOM learning resource type; it is kept verbatim and the LOM element is left empty