AI in Education

Exploring artificial intelligence tools and their applications in teaching and learning

The Evolution of AI in Education

Early AI & Intelligent Tutoring Systems (1950s-1980s)

The concept of AI emerged in the 1950s, with the term "artificial intelligence" coined at the 1956 Dartmouth Conference. Early educational applications began appearing in the 1970s with the development of Intelligent Tutoring Systems (ITS), which aimed to provide personalised instruction by simulating one-to-one human tutoring.

Key Developments:

  • SCHOLAR system (1970) by Jaime Carbonell for teaching geography
  • BUGGY system (1978) designed to identify student misconceptions in mathematics
  • GUIDON system (1979) for medical education

These early systems made rudimentary attempts to model student knowledge and adapt instruction, laying the groundwork for future intelligent educational technology.

Adaptive Learning & Expert Systems (1980s-1990s)

The 1980s and 1990s saw the rise of expert systems and more sophisticated adaptive learning platforms. These systems could analyse student responses and provide different paths through learning materials based on performance.

Key Developments:

  • Carnegie Learning's Cognitive Tutor (1980s) for mathematics instruction
  • Educational expert systems that could solve and explain domain-specific problems
  • AutoTutor (late 1990s) using natural language processing for tutoring

During this period, AI systems became better at modelling domains of knowledge and tracking student progress, though they were still limited in their flexibility and naturalness.

Web-Based Learning & Data Mining (2000s)

The 2000s marked the integration of AI with web-based learning systems and the beginning of educational data mining. With the growth of online learning, vast amounts of learner data became available for analysis.

Key Developments:

  • Emergence of learning management systems with basic adaptive features
  • Educational data mining to identify patterns in student learning
  • Recommender systems for educational resources
  • Knewton's adaptive learning platform (2008)

This era saw AI moving beyond simple rule-based systems to more sophisticated statistical models that could detect patterns across large datasets.

Personalised Learning & MOOCs (2010-2015)

The early 2010s witnessed the explosion of Massive Open Online Courses (MOOCs) and a growing emphasis on personalised learning paths. AI became increasingly important for managing and navigating the vast content libraries of these platforms.

Key Developments:

  • Launch of major MOOC platforms (Coursera, edX, Udacity) with basic AI features
  • Smart content recommendation in adaptive learning systems
  • Automated essay scoring systems for at-scale assessment
  • Carnegie Learning's enhanced mathematics tutors using AI

These developments made education more accessible globally while beginning to address the challenge of personalisation at scale.

Deep Learning & Intelligent Tutors (2015-2020)

The mid-2010s saw breakthroughs in deep learning and neural networks that revolutionised AI capabilities, including in education. More sophisticated interactive systems emerged that could better understand and respond to learners.

Key Developments:

  • ALEKS and other adaptive learning platforms refined their algorithms
  • Improved natural language processing for educational applications
  • Machine learning for predicting student outcomes and identifying at-risk learners
  • Duolingo's adaptive language learning system

The ability of these systems to process natural language and adapt to individual learning patterns improved significantly during this period.

Large Language Models & Generative AI (2020-Present)

The current era is defined by the emergence of powerful large language models (LLMs) and generative AI that have transformed what's possible in educational technology. These systems can understand and generate human-like text, code, and other content.

Key Developments:

  • ChatGPT and similar conversational AI tools for tutoring and content creation
  • AI teaching assistants that can answer student questions
  • Personalised curriculum and assessment generation
  • AI-powered tools for educators (lesson planning, resource creation)
  • Khan Academy's Khanmigo and other specialised educational AI assistants

These powerful new AI tools offer unprecedented capabilities for personalized learning, content creation, and educational support, though they also bring challenges related to accuracy, ethics, and appropriate use.

AI for Learning Technologists

How AI Can Support Learning Technology Work

As a learning technologist, AI tools can significantly enhance your workflow and capabilities across multiple areas of responsibility. Here's how AI can be particularly valuable in your role:

Content Creation

Create first drafts of learning materials, generate assessment questions, develop scenarios for simulations, craft discussions prompts, and produce accessible content variants.

Teaching Support

Build personalised learning paths, create scaffolded activities, develop simple simulations, and generate supplementary explanations for difficult concepts.

Technical Work

Generate code snippets for learning platforms, troubleshoot technical issues, create templates and workflows, and develop simple plugins or extensions.

Research & Evaluation

Analyse feedback data, summarise research papers, track technology trends, and help interpret complex learning analytics.

Best Practices for Using AI in Educational Settings

  • Verify accuracy: Always review and fact-check AI-generated content before using it with learners
  • Maintain authenticity: Use AI as a starting point that you adapt and personalise, not as a replacement for your expertise
  • Consider ethical implications: Be transparent about AI use and ensure it aligns with institutional policies
  • Focus on enhancement: Use AI to augment human capabilities rather than replace human connection
  • Model appropriate use: Demonstrate and teach responsible AI use to colleagues and learners

Essential AI Tools for Learning Technologists

Below is a curated collection of AI tools particularly valuable for learning technologists. Each tool includes a brief description, pricing model, and link to the official website. Use the category filters to focus on specific types of tools.

ChatGPT

Freemium
Content Creation Research

A powerful conversational AI that can generate text, answer questions, brainstorm ideas, and assist with writing tasks across various domains. Useful for drafting learning materials, assignment instructions, and explanations.

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Google Gemini

Freemium
Content Creation Research

Google's AI assistant that can draft content, answer queries, and process both text and images. Helpful for educational content creation with up-to-date information access.

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Grammarly

Freemium
Writing Proofreading

AI-powered writing assistant for grammar, spelling, and style suggestions. Essential for ensuring learning materials and communications are error-free and professionally written.

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Notion AI

Paid
Content Creation Organisation

Integrated AI within Notion that helps generate content in notes and documents. Useful for summarising materials, creating lesson plans, and organising educational resources.

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DALLĀ·E (via Bing)

Free
Image Generation Visual Content

AI image generator that creates visuals from text descriptions. Perfect for creating custom illustrations for learning materials when stock photos won't suffice.

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Canva (Magic Studio)

Freemium
Design Content Creation

Design platform with AI tools for creating presentations, infographics, and educational materials. Features include text-to-image, background removal, and text generation.

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Remove.bg

Freemium
Image Editing

AI tool for instantly removing backgrounds from images. Extremely useful for creating professional-looking visual resources for courses and presentations.

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Synthesia

Paid
Video Generation Presentation

Creates realistic AI videos from text scripts. Learning technologists can use it to quickly produce video content for modules without filming equipment.

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Descript

Freemium
Video Editing Audio Editing

AI-powered audio/video editor that works by editing text transcripts. Allows for easy editing of instructional videos and includes features like voice cloning for corrections.

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ElevenLabs

Freemium
Text-to-Speech Audio

Realistic AI voice generation tool for creating high-quality narration for educational content. Offers various voices and emotional tones for engaging audio.

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MagicSchool AI

Freemium
Education Teaching

Collection of AI tools specifically designed for educators, offering functions for lesson planning, assessment creation, and differentiated materials development.

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Quizlet

Freemium
Assessment Study Tools

Study platform with AI features for creating flashcards, practice tests, and adaptive learning experiences. The Q-Chat feature allows interactive study sessions.

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Curipod

Freemium
Lesson Creation Interactive

AI-powered tool for creating interactive lessons and activities. Learning technologists can use it to quickly generate engaging teaching materials and formative assessments.

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Perplexity AI

Free
Research Information

AI-powered answer engine that responds to queries with cited sources. Excellent for conducting quick research on educational topics and discovering reliable sources.

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Elicit

Free
Research Academic

AI research assistant that helps find and summarize academic papers. Valuable for learning technologists keeping up with educational technology research or conducting literature reviews.

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ChatPDF

Freemium
Document Analysis Research

Tool that allows you to upload a PDF and ask questions about its content. Useful for quickly extracting information from educational research papers, textbooks, or policy documents.

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Tome

Freemium
Presentation Content Creation

AI-powered storytelling and presentation tool that generates visual narratives from text prompts. Great for creating engaging educational presentations and instructional materials.

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Lumen5

Freemium
Video Creation Content Conversion

Video creation platform that uses AI to transform text content into videos. Learning technologists can use it to convert module content into engaging video modules easily.

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Scholarcy

Freemium
Research Summarization

AI summarizer designed for academic papers that creates summary flashcards highlighting key points. Ideal for digest research for educational technology decisions and staying current with research.

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Ethical Considerations for AI in Education

Navigating the Ethical Use of AI in Learning Technology

While AI offers powerful capabilities for learning technologists, it's essential to consider ethical implications when implementing these tools in educational contexts:

Equity & Access

Consider whether AI tools are accessible to all learners, including those with disabilities or limited technology access. Ensure that AI implementation doesn't create or amplify digital divides.

Privacy & Data Protection

Be mindful of how student data is collected, stored, and used by AI systems. Comply with educational data protection regulations (like GDPR) and maintain transparent data practices.

Accuracy & Reliability

Recognize that AI systems can produce incorrect or misleading information ("hallucinations"). Always verify AI-generated content before using it in educational settings, and teach critical evaluation skills.

Cognitive Development

Consider how AI use affects the development of critical thinking, problem-solving skills, and creativity. Design learning experiences where AI enhances rather than replaces these cognitive processes.

Human Connection

Maintain the importance of human relationships in education. Use AI to free up time for meaningful human interactions rather than replacing them entirely.

Institutional Policies

Familiarize yourself with your institution's AI policies and guidelines. Help develop clear frameworks for appropriate AI use if they don't yet exist.

Developing an AI Policy

As a learning technologist, you may be involved in creating institutional AI policies. Consider including these elements:

  • Clear guidelines on acceptable AI use for staff and students
  • Transparency requirements about when and how AI is used
  • Assessment design considerations to maintain academic integrity
  • Professional development to support ethical AI implementation
  • Regular review processes to evaluate AI impacts and address emerging concerns