Your Name and Title:

Daniel Bernstein, CEO, Teachally

School, Library, or Organization Name:


Area of the World from Which You Will Present:

USA, Boston

Language in Which You Will Present:


Target Audience(s):

Teachers, Administrators, Educational Consultants, Curriculum Developers, EdTech Enthusiasts.

Short Session Description (one line): Teaching with AI: Revolutionizing Education for the Future


Full Session Description (as long as you would like):

Artificial Intelligence (AI) is revolutionizing education, offering transformative potential for teachers and students alike. In this session, "Teaching with AI: Revolutionizing Education for the Future," we will delve into the pressing challenges facing educators today, from teacher turnover to outdated and scarce educational resources. We will explore how AI can help alleviate these issues through ethical, personalized, and efficient teaching practices.

We will begin by tracing the historical journey of AI, from the Dartmouth Conference of 1956 to the rise of Generative AI, which emerged as the culmination of decades of research. We will discuss the versatile nature of generative models and their ability to generate nuanced, original content based on vast datasets.

Next, we will navigate the challenges and misconceptions surrounding AI in education, addressing concerns about bias, plagiarism, privacy, and copyright. We will emphasize the importance of ethical guidelines and guardrails to prevent misinformation while exploring how AI can enhance, not replace, the vital role of teachers.

We will then highlight best practices for leveraging AI in education, including personalized learning and differentiation, efficiency and automation in lesson planning, and data-driven instructional strategies. Teachers can reclaim valuable hours through AI-powered automation, allowing them to focus on fostering student engagement and creativity.

"Teaching with AI: Revolutionizing Education for the Future"


  • Slide 1: Addressing Pressing Challenges in Education
    • Teacher turnover and its impact on educational quality.
    • Outdated and scarce educational resources.
    • Misalignment of curriculum materials with state and national standards.
    • Time constraints hindering personalized instruction.

Historical Context of AI

  • Slide 2: The Journey of Artificial Intelligence
    • Dartmouth Conference (1956) and the inception of AI.
    • Cycles of optimism and "AI winters."
    • Generative AI emerges as the culmination of decades of research.
  • Slide 3: Generative AI Models Overview
    • Versatile, Swiss Army knife-like nature of generative models.
    • Training on vast datasets to create nuanced, original content.

Navigating Generative AI in Education

  • Slide 4: Generative AI and the Importance of User Expertise
    • Importance of skilled handling to ensure quality outputs.
    • Potential for hallucinations due to eagerness to please.
  • Slide 5: Training Generative AI Models with Ethical Guidelines
    • Claude by Anthropic: apprentice-mentor training.
    • ChatGPT: rigorous training with ethical and factual standards.
  • Slide 6: The Chat Interface and Its Educational Limitations
    • Flexibility and potential for lesson planning.
    • Importance of clear prompts for curriculum alignment.
    • Lack of context persistence affecting lesson continuity.

Common Concerns and Misconceptions about AI

  • Slide 7: Misconceptions about Generative AI
    • Bias and misinformation due to data training.
    • Plagiarism concerns with AI-generated content.
    • Privacy issues and copyright challenges.
  • Slide 8: AI in Teaching - Role of Educators
    • Teachers are irreplaceable due to their unique human qualities.
    • AI can augment and enhance, not replace, the teaching role.

Guardrails and Ethical Challenges

  • Slide 9: Training AI Models with Guardrails
    • Preventing biases and misinformation with ethical guidelines.
    • Google’s diversity initiative and its challenges.
  • Slide 10: Plagiarism in the Age of AI
    • Lack of reliable AI detection tools.
    • Embracing collaborative learning and peer review.
    • Evolving teaching methods to foster personal reflection.
  • Slide 11: Privacy Concerns in AI-Enabled Education
    • Protecting personally identifiable information (PII).
    • Importance of anonymization and responsible data handling.
  • Slide 12: Copyright and Ownership in AI-Generated Content
    • OpenAI and Claude’s differing stances on copyright.
    • Caution required in feeding data into models.

Principles and Best Practices

  • Slide 13: Personalized Learning and Differentiation with AI
    • Differentiated learning paths and inclusive education.
    • Using AI to identify and address learning gaps.
  • Slide 14: Efficiency and Automation in Lesson Planning
    • Reclaiming hours lost to administrative tasks.
    • Automating lesson planning, grading, and assessment.
  • Slide 15: Standards Alignment and Data-Driven Instruction
    • Aligning lesson plans with state and national standards.
    • Using AI to identify trends and improve instructional strategies.
  • Slide 16: Ethical and Responsible Use of AI in Education
    • Implementing guardrails to ensure accurate, unbiased outputs.
    • Safeguarding privacy and respecting copyright.


  • Slide 17: Future of Teaching with AI
    • Preparing for the evolving role of teachers as facilitators.
    • Embracing AI as a complementary tool to enhance education.


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