An educator’s perspective
Beyond the burden: AI in education
Adapted from Kenji Nomura’s Beyond the Burden: Practical AI for Teacher Success and Student Readiness. The session makes space for concerns, shared language, and practical teaching ideas. It invites thoughtful choices about how much to engage with AI.
Seven concerns worth discussing
These are the questions Kenji raises in slides 9–16. The discussion prompts below are this hub’s adaptation. Linked research, guidance, and official records offer additional perspectives.
Accuracy and verification
From the presentation, slide 10: Kenji raises concerns about confident errors and opaque reasoning, while emphasizing verification and critical thinking.
A question to use: Which claims, calculations, and references can you check against an original source? Who reviews an answer before it affects a learner?
Bias and representation
From the presentation, slide 11: The presentation asks how models reinforce existing biases and whose experiences they represent.
A question to use: Whose language, identity, or experience is missing? Would the answer change if you varied the names, examples, or circumstances?
Privacy and data use
From the presentation, slide 12: Kenji questions where data moves, whether it enters training, and what an opt-out actually controls.
A question to use: What does this specific account and service retain or share? Can you do the task with fictional or public examples instead of student records?
Ownership, authorship, and citation
From the presentation, slide 13: The presentation distinguishes producing an output from creating it and asks about inspiration, impersonation, and training material.
A question to use: What did the person contribute? What assistance should be disclosed? What permissions and attribution apply to the input and output?
Environmental impact and informed abstinence
From the presentation, slide 14: Kenji highlights land, power, heat, and e-waste, and includes both responsible use and informed abstinence as choices.
A question to use: What system and workload does an impact estimate describe? Which local costs matter, and who bears them? Is a smaller tool or an approach without AI sufficient?
Equity and access
From the presentation, slide 15: The presentation weighs paid tools and unequal training or support against access to inexpensive professional tools.
A question to use: Can everyone take part without a paid account? What support or accessible alternative is available? Who benefits from the time saved?
Effort, mastery, and academic integrity
From the presentation, slide 16: Kenji asks whether easy answers displace practice, how we define mastery, and whether an enforcement arms race helps learning.
A question to use: What should a learner be able to do independently? Where does assistance support practice? How will discussion, drafts, or demonstrations make learning visible?
Learning links from the presentation
FLCC’s FLX AI Hub ↗ · Authentic AI Literacy ↗ · Google’s educator course ↗ · IBM’s AI introduction ↗
FLCC serves New York and offers online resources; it is not a DMV provider. Authentic AI Literacy offers educator-led training. Google and IBM are vendor sources. Confirm enrollment and local professional-development credit separately.
Source: the 22-slide PowerPoint and PDF supplied by Kenji, reviewed October 11, 2026. Slide references identify his presentation themes; the prompts are editorial adaptations. This section does not claim that his presentation establishes research findings or represents a school system’s policy.