Please Note..

This webinar and the engagement tools will be recorded.

An archive will be available on the event website. 

1
NICE Webinar: 

The Impact of Generative 

Artificial Intelligence on 

Education and Workforce

September 20, 2023
H. Chad 

Lane

Elyse 

Ashburn

Michael 

Prebil

Katie Hall

3
AI In Education: 

Human Learning, 40+ years of work, and Generative AI

H. Chad Lane

Associate Professor of Educational Psychology & 

Computer Science

University of Illinois, Urbana-Champaign

hclane@Illinois.edu

http://hchadlane.net

@hchadlane

4 NIST-NICE Webinar – September 20, 2023
Context: Three (related) priority areas 

AI applied to education:

 Use AI to enhance 

learning, improve 

educational 

technologies, and 

support teachers

How can we educate 

people about AI?

 AI literacy

 K-12 (ai4k12.org), 

workforce, higher 

education

How can we use AI to 

improve our own 

learning?

 Using LLMs to 

teach/coach 

ourselves

5

Three take-aways

Everything we do should be based on the science of learning 

AI in Education has a long history! 

Generative AI holds profound promise, but we need research.

7
• Human Learning can be a challenge 

to investigate and understand.

• Our intuitions and feelings can be 

radically misleading!

• What does it mean to have evidence 

about how we learn? What helps? 

• Do you believe in any myths?

 https://n.pr/38dTNKu

 Scroll to beginning of quiz... try it!

GRAINGER ENGINEERING

The Scientific Study of Learning

8
• Learning is constructive – it requires effort!

• Pre-existing knowledge/preconceptions matter

• Metacognition, self-regulatory, and noncognitive 

skills are learned too.

• Learning is shaped by culture and social factors 

• Motivation underlies effective learning, interest 

and sense of belonging matter

• Effective instruction involves formative feedback 

and use of relevant/meaningful content

The Learning Sciences: We know a lot about how human learning works!

FREE! National Academies Press (2001, 2018) 

9
Three take-aways

Everything we do should be based on the science of learning 

AI in Education has a long history! 

Generative AI holds profound promise, but we need research.

10
~50 years of AIED

International AIED Society

https://iaied.org/

Cognitive Tutors (CMU) SCHOLAR (1970s)

1987 2009

Pedagogical Agents

(credit: Ada & Grace, 2011)

2023

11
Two of my favorite studies 

VCR Tutor (Mark & Greer, 1985)

Increasing AI  Increased Learning

Alex (Finkelstein, Cassell, et al., 2013)

Dialect Matching  Increased Learning 12
Our Solar System

Exoplanets

Hypothetical Earths

Exploration

Observations

Taking measurements

Building habitats

Talking to Scientists

Quasars/Galactic Structures

Awards #1713609

and #1906873
How we leverage AI in WHIMC:

• To judge observations made in Minecraft

 Convolutional Neural Networks (CNNs) pre- trained to assess images from Minecraft and 

matched to automated descriptions.

• To assess learner progress

 Bayesian Knowledge Tracing (BKT) – tracking 

acquisition of observation-making skills

• To interact with learners via NPCs

 Mixed menu-based, open-ended Q&A 

interactions; positive/enthusiastic dialogue

• To assess habitat construction

 NN-based, combination of visual and game 

play data

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https://invite.illinois.edu/

Award #2229612

AI Institute for Inclusive and Intelligent 

Technologies for Education

15
Three take-aways

Everything we do should be based on the science of learning 

AI in Education has a long history! 

Generative AI holds profound promise, but we need research.

16
Generative AI for Education

• Two key challenges:

 Accuracy – LLM output is difficult to 

verify automatically

 Pedagogy – without careful 

prompting, LLMs provide information, 

they don’t tend to do anything to 

help you construct knowledge

• Other interesting LLMs worth 

considering:

 Pi.ai (helpful, upbeat, friendly)

 Agentgpt.reworkd.ai (breaks tasks 

into subtasks)

 Claude.ai (upload pdfs to ask about

 Questgen.ai (generates quizzes from 

text)

 And that’s just a few!

Mollick, Ethan R. and Mollick, Lilach, Assigning AI: Seven 

Approaches for Students, with Prompts (June 12, 2023). 

17 Available at SSRN: https://ssrn.com/abstract=4475995
Thank you!

Everything we do should be based on 

the science of learning 

AI in Education has a long history! 

Generative AI holds profound promise, 

but we need research.

18
Q&A

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NIST Writing and Resources on AI

• Artificial Intelligence Risk Management Framework (AI RMF 1.0) (Jan. 2023)

https://www.nist.gov/itl/ai-risk-management-framework

See also: 

o Trustworthy & Responsible AI Resource Center: https://airc.nist.gov/Home

o Sep. 2023 TIME profile of Elham Tabassi, NIST Senior Research Scientist & Associate Director for 

Emerging Technologies: https://time.com/collection/time100-ai/6310638/elham-tabassi/

• Towards a Standard for Identifying and Managing Bias in AI (NIST SP 1270) (March 2022) 

https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf

• Four Principles of Explainable Artificial Intelligence (NIST IR 8312) (Sep. 2021) 

https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8312.pdf

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Submit an event survey!

https://www.surveymonkey.com/r/Sept2023NICEWebinar
NICE Webinar Series & Special Events

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(October 17, 2023, 4:00-4:45PM ET)

Cultivating Cybersecurity Leaders: The New Cybersecurity Career Ambassador Program (October 18, 2023, 2:00-3:00PM ET)

nist.gov/nice/webinars 22