Teaching in the AI Era
Practical Strategies for the Modern Classroom
Summary
It explains the technology in plain language then offers concrete, evidence-informed strategies for prompt engineering, lesson planning, materials creation, assessment design, academic integrity, and the human-in-the-loop oversight that responsible classroom use requires. Case studies from K–12 and higher education show what works, what fails, and why. The result is a measured handbook for teachers, teacher educators, school leaders, instructional designers, and academic-integrity professionals navigating the move from AI as an outside force to AI as a present feature of everyday teaching.
Excerpt
Table Of Contents
- Cover
- Title Page
- Copyright Page
- Contents
- List of figures
- List of tables
- Acknowledgements
- List of abbreviations
- Chapter 1 Introduction: The landscape of AI in education
- 1.1 A quick scene-setting: Why AI feels ‘suddenly’ everywhere?
- 1.2 Key ideas in plain language: Terms that will be used throughout the book
- 1.3 A short history of AI in educational technology
- 1.4 What AI is doing in classrooms and campuses today
- 1.5 What the evidence says so far: Benefits and limits
- 1.6 Why adoption accelerated: Technical, institutional, and cultural drivers
- 1.7 Ethical and equity questions that cannot be postponed
- 1.8 Practical principles for responsible use: A ‘good questions’ checklist
- 1.9 Conclusion
- Chapter 2 Understanding large language models: A guide for non-technical educators
- 2.1 What is a large language model?
- 2.2 How do LLMs learn? Training and fine-tuning
- 2.3 The architecture behind LLMs: Transformers and attention
- 2.4 What LLMs can and cannot do: Capabilities and limitations
- 2.5 Prompting: How to communicate with LLMs
- 2.6 Common misconceptions about LLMs
- 2.7 Implications for teaching and learning
- 2.8 Current models and their educational applications (2026)
- 2.9 Multimodal models: Beyond text
- 2.10 Parameters, scale, and the question of ‘bigger is better’
- 2.11 Conclusion: Towards informed and responsible use
- Chapter 3 The art of prompt engineering in pedagogy
- 3.1 Introduction: Why prompt engineering matters for educators
- 3.2 Fundamentals of prompt engineering
- 3.2.1 What makes a good prompt?
- 3.2.2 Anatomy of an educational prompt
- 3.2.3 The prompt engineering workflow
- 3.3 Core prompting techniques for education
- 3.3.1 Zero-shot prompting
- 3.3.2 Few-shot prompting
- 3.3.3 Chain-of-thought prompting
- 3.3.4 Role-based prompting
- 3.3.5 Structured output prompting
- 3.3.6 Comparison of core techniques
- 3.4 Pedagogical frameworks for prompt design
- 3.4.1 The PROMPT framework
- 3.4.2 The BPE framework
- 3.4.3 Advanced assessment frameworks (PedCoT & CoTAL)
- 3.4.4 Integrating Bloom’s Taxonomy
- 3.4.5 Socratic questioning through prompts
- 3.4.6 Comparison of pedagogical frameworks
- 3.5 From theory to practice: Your prompt library
- 3.6 Advanced techniques and strategies
- 3.6.1 Multi-step prompt chaining
- 3.6.2 Iterative refinement and human-in-the-loop approaches
- 3.6.3 Context management and chunking
- 3.6.4 Parameter tuning for educational applications
- 3.6.5 Retrieval-augmented generation (RAG)
- 3.7 Common pitfalls and how to avoid them
- 3.7.1 Vague and ambiguous prompts
- 3.7.2 Over-prompting versus under-prompting
- 3.7.3 Hallucinations and bias amplification
- 3.7.4 Academic integrity and quality control
- 3.8 Teaching prompt engineering to students
- 3.9 Subject-specific applications
- 3.9.1 STEM education (Science, Technology, Engineering, Mathematics)
- 3.9.2 Humanities and social sciences
- 3.9.3 Medical and health sciences
- 3.9.4 Arts and creative disciplines
- 3.10 Best practices and next steps
- Chapter 4 AI for lesson planning and administrative tasks
- 4.1 Introduction: The promise of time
- 4.2 Understanding teacher workload: The context for AI adoption
- 4.3 High-value use case 1: AI-assisted lesson planning
- 4.3.1 How teachers are using AI for lesson planning
- 4.3.2 Evidence of effectiveness
- 4.3.3 Practical workflow: The lesson planning pipeline
- 4.3.4 Subject-specific considerations
- 4.3.5 Common pitfalls and solutions
- 4.4 High-value use case 2: Creating instructional materials
- 4.4.1 AI capabilities and limitations by material type
- 4.4.2 Quality considerations
- 4.4.3 Workflow: Materials creation pipeline
- 4.4.4 Differentiation and subject-specific applications
- 4.5 High-value use case 3: Assessment design and feedback
- 4.5.1 Quality and validity concerns
- 4.5.2 Automated feedback systems
- 4.5.3 Practical workflow: Assessment pipeline
- 4.5.4 Feedback generation best practices
- 4.6 High-value use case 4: Administrative task automation
- 4.6.1 Evidence of time savings
- 4.6.2 Practical applications
- 4.6.3 Quality control for administrative AI use
- 4.6.4 Integration with school systems
- 4.7 Maintaining pedagogical quality: The human-in-the loop model
- 4.7.1 The human-in-the-loop workflow
- 4.7.2 Quality control checklists
- 4.7.3 Building institutional safeguards
- 4.8 Addressing ethical and professional concerns
- 4.8.1 Professional autonomy and deskilling
- 4.8.2 Bias, accuracy, and cultural responsiveness
- 4.8.3 Data privacy and security
- 4.8.4 Equity of access
- 4.8.5 Intellectual property
- 4.9 Professional development and implementation strategies
- 4.9.1 Implementation roadmap
- 4.9.2 Building communities of practice
- 4.9.3 Measuring impact
- 4.10 Conclusion: Strategic, responsible, and human-centred AI use
- Chapter 5 Student assessment in the AI era
- 5.1 Why assessment feels different now
- 5.2 Integrity, disclosure, and the limits of detection
- 5.3 Evidence of learning and the three-mode model
- 5.4 Redesigning coursework so that learning is visible
- 5.5 Feedback cycles and rubric redesign
- 5.6 Building capability, policy clarity, and proportionate security
- 5.7 Worked examples and closing perspective
- Chapter 6 Ethics, plagiarism, and academic integrity
- 6.1 Redefining academic integrity in the AI era
- 6.2 The detection dilemma: Why AI detectors fail
- 6.3 Institutional policies and guidelines
- 6.4 Pedagogical responses: Redesigning assessment
- 6.5 Teaching ethical AI use and building integrity
- 6.6 Equity, bias, and access
- 6.7 Faculty perspectives and challenges
- 6.8 Positive and ethical AI use for learning
- 6.9 The future of assessment and integrity
- 6.10 Conclusion: Towards a culture of integrity
- Chapter 7 Case studies: Success stories and failures
- 7.1 K–12 success stories
- 7.2 Higher education success stories
- 7.3 AI tutoring and personalised learning: Deep dive
- 7.4 Writing and language learning success
- 7.5 STEM education applications
- 7.6 Accessibility and equity outcomes
- 7.7 Failures, challenges, and what went wrong
- 7.8 Lessons learned and best practices
- 7.9 Conclusion: From cases to action
- Chapter 8 Future trends: Where is AI education heading?
- 8.1 From occasional tool to everyday learning partner
- 8.2 Personalisation grows up: Multimodal tutoring, learning analytics, and new feedback loops
- 8.3 Assessment and credentials in an AI-rich world
- 8.4 Teachers and institutions: New work, new competencies, and new support structures
- 8.5 Equity, access, privacy, and the hidden costs of ‘free help’
- 8.6 Governance and regulation: Trust becomes a design requirement
- 8.7 What to watch next: Likely scenarios for the next decade
- Bibliography
- Appendix: Ready-to-use prompt templates for common teaching tasks
- Index
Figures
Figure 1.1: Roles of AI in education
Figure 2.1: The multi-stage training process of large language models, progressing from initial pre-training to human-aligned fine-tuning
Figure 3.1: The human-in-the-loop workflow demonstrating mandatory human oversight throughout the AI generation process
Figure 3.2: Prompt engineering troubleshooting flowchart
Figure 3.3: Subject-specific prompting decision flow
Figure 5.1: Taxonomy of assessment modes and their corresponding evidence of learning in the generative AI era
Figure 6.1: The spectrum of AI use in academic work, ranging from strictly prohibited to actively encouraged
Figure 6.2: The paradigm shift in managing artificial intelligence in educational settings
Figure 7.1: A four-phase implementation framework for integrating artificial intelligence in educational institutions
Figure 8.1: Key future trends shaping the integration and evolution of artificial intelligence in educational settings
Tables
Table 2.1: Major language models for education (February 2026)
Table 3.1: Comparison of core prompting techniques
Table 3.2: Pedagogical frameworks for prompt engineering
Table 4.1: Teacher time allocation and AI potential
Table 4.2: AI effectiveness for different material types
Table 4.3: Administrative tasks AI suitability matrix
Table 5.1: Assessment redesign strategies
Table 5.2: Three assessment modes for GenAI-aware coursework
Table 5.3: Quick menu of redesign patterns
Table 5.4: GenAI use log template
Table 6.1: Spectrum of AI use in academic work
Table 6.2: AI detection tools, claims vs reality
Table 8.1: Key future trends and what they imply for everyday learning
Acknowledgements
We thank the Faculty of Informatics and Management at the University of Hradec Králové for institutional support, the colleagues in the Department of Applied Linguistics and the Department of Informatics and Quantitative Methods for ongoing exchange, and the anonymous reviewer whose detailed report shaped the final form of this manuscript. This book was written within the UHK Prestige project 2026.
AI-based language tools were used by the authors in a limited supporting role only, for minor stylistic and linguistic refinements (such as grammar checks and copy-edit suggestions for sentence-level clarity) and for assistance with Python scripts used to programmatically generate the book’s data-driven figures. All content of the manuscript, its argument, structure, case selection, analytical claims, interpretations, citations, and conclusions, is the original scholarly work of the authors. Every factual claim and every citation have been verified by the authors against primary, peer-reviewed or otherwise authoritative published sources, and the authors take full responsibility for the manuscript. No AI image-generation tool was used to produce, augment, or modify any figure, illustration, or cover image; all ten figures in the book are deterministic, programmatically generated diagrams produced from author-written code.
Every effort has been made to trace copyright holders and to obtain their permission for the use of copyright material. The publisher apologises for any errors or omissions in the above list and would be grateful for notification of any corrections that should be incorporated in future reprints or editions of this book.
Abbreviations
AI Artificial Intelligence
API Application Programming Interface
BPE Best Practices in Education (prompt framework)
CAI Computer-Assisted Instruction
CMYK Cyan, Magenta, Yellow, Key (black) – print colour model
CoT Chain-of-Thought
CoTAL Chain-of-Thought with Active Learning
DPI Dots Per Inch
EAP English for Academic Purposes
EDM Educational Data Mining
ELL English Language Learner
EU European Union
FERPA Family Educational Rights and Privacy Act
GenAI Generative Artificial Intelligence
GPA Grade Point Average
GPT Generative Pre-trained Transformer
GPU Graphics Processing Unit
IEP Individualised Education Programme
ISBN International Standard Book Number
ITS Intelligent Tutoring System
K–12 Kindergarten through Grade 12 (primary and secondary education)
LA Learning Analytics
LLM Large Language Model
LMS Learning Management System
ML Machine Learning
MMLA Multimodal Learning Analytics
NLP Natural Language Processing
OECD Organisation for Economic Co-operation and Development
PedCoT Pedagogically guided Chain-of-Thought
PROMPT Purpose, Role, Output, Materials, Parameters, Tone (prompt framework)
RAG Retrieval-Augmented Generation
Details
- Pages
- XIV, 172
- Publication Year
- 2026
- ISBN (PDF)
- 9781805846819
- ISBN (ePUB)
- 9781805846826
- ISBN (Softcover)
- 9781805846802
- DOI
- 10.3726/b24013
- Language
- English
- Publication date
- 2026 (October)
- Keywords
- artificial intelligence in education generative AI ChatGPT in the classroom large language models prompt engineering lesson planning materials creation assessment design academic integrity AI literacy teacher workload ethical AI educational technology human-in-the-loop K-12 education higher education instructional design
- Published
- Oxford, Berlin, Bruxelles, Chennai, Lausanne, New York, 2026. xiv, 172 pp., 10 fig. col., 13 tables.
- Product Safety
- Peter Lang Group AG
