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Artificial Intelligence Goes to School

Location: W176bc

Listen and learn

Listen and learn : Lecture

Monday, June 25, 12:00–1:00 pm
Location: W176bc

Hina Baloch   Dr. Helen Crompton   Mark Gerl   Sharon Harrison   Daniel Law   Frederique McGirt   Dr. Yolanda Ramos   Dr. Joseph South  
ISTE and General Motors Corporate Giving are collaborating in a pilot program that cultivates future artificial intelligence (AI) programmers and provides professional learning for K-12 educators to support student-driven AI explorations in the classroom. In this panel, teachers will share strategies to integrate AI in project-based learning.

Skill level: Beginner
Attendee devices: Devices not needed
Focus: Digital age teaching & learning
Topic: Computer science and computational thinking
Grade level: 6-12
Subject area: STEM/STEAM, Computer science
ISTE Standards: For Educators:
  • Use technology to create, adapt and personalize learning experiences that foster independent learning and accommodate learner differences and needs.
For Students:
Empowered Learner
  • Students understand the fundamental concepts of technology operations, demonstrate the ability to choose, use and troubleshoot current technologies and are able to transfer their knowledge to explore emerging technologies.
Computational Thinker
  • Students understand how automation works and use algorithmic thinking to develop a sequence of steps to create and test automated solutions.

Proposal summary

Purpose & objective

This panel will report out on ISTE-GM pilot learning program goals:

• Articulate a basic understanding of different artificial intelligence components, what they are, how they work and their current applications.

• Describe how exploring artificial intelligence concepts and applications with students could support the development of project-based learning, STEM skills and career awareness, digital fluency in the classroom and critical use of technology.

• Identify and apply specific tools and approaches for using artificial intelligence to support interdisciplinary teaching.

• Use a project-based learning framework to design a project-based unit that applies artificial intelligence to solve a problem or enable creative learning and new forms of expression.

• Reflect on ways to leverage artificial intelligence applications to support student achievement and nurture students’ interests and talents in computer science.


A panel discussion on the process and outcomes of this professional learning experience based on the above goals.

Supporting research

Although domination in Go by AI may be interesting, it is hardly a practical technological advancement for a society facing many complex problems. But the principles underlying the development of AlphaGo pertain to fields in almost any sector of human society. Smart chatbots can easily help customers navigate financial transactions and improve fraud protection and security around the clock in any time zone. Self-driving cars can reduce incidence of vehicular collision. Medical applications can effectively search massive databases and inform diagnoses, especially of rare conditions. And the list could goes on.

The International Society for Technology in Education (ISTE) constantly seeks to explore, understand, and discuss emerging trends in technology and education in our rapidly changing world. One such area is AI, specifically AI fueled by deep learning or what are known as “deep neural networks.” If AI is broadly defined as applying techniques that enable computers to mimic human intelligence (such as logic), then deep learning defines a subset of AI embedded with a learning-specific algorithm that can make sense out of huge amounts of data and then draw its own conclusions. Deep learning is a subset of “machine learning,” although not all machine learning relies on these deep neural networks to compute (Parloff, 2016).

Today, deep learning is used to train computers in areas such as speech and image recognition. Such learning is accomplished through “neural nets,” which nest layers of learning and recognition inside each other to effectively identify an output. Compared to humans, AI offers both advantages and disadvantages in such learning. Humans are able to develop effective pattern recognition strategies with only a few instances of data and explain why we make decisions. We are hardwired to easily do tasks like speaking and recognizing faces, which are incredibly challenging for machines, including computers, to do.

While AI requires much more information than do humans to understand patterns, that same wealth of data has the potential for superior recognition. It can be deployed in ways that make human life easier, more productive, or more fun. Yet, despite this deep learning, AI today cannot tell us why it reached a decision, even if its decision is correct (Melendez, 2016).

ISTE's literature review focuses on implications AI offers for education, including the personalization of learning and supporting predictive modeling using data analytics. But the intersection between AI within schools and AI outside of schools also deserves attention, since our schools need to prepare today’s students for the careers of tomorrow—and AI will be one of those careers. Further, the implications that AI has for human life are vast, so in order to prepare students for the future, we have to set them up to be knowledgeable and critical users of AI.

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Hina Baloch, General Motors

Hina Baloch is a Manager of Global Social Impact and STEM Education at General Motors Corporate Giving. She is an international development expert with extensive experience of designing and implementing innovative social sector programs. Experienced in quantitative and qualitative research design, data analysis, strategy development, new partnership management, community engagement, university-community partnership, higher-ed internationalization and corporate social investment strategies in the areas of education, youth employment and child protection.

Dr. Helen Crompton, Old Dominion University
Mark Gerl, Fulton Academy of Science and Technology
Sharon Harrison, University of Chicago Laboratory Schools
Daniel Law, Lane Tech College Prep High School
Frederique McGirt, Atlanta International School
Dr. Joseph South, ISTE

Joseph South, chief learning officer, is a strategic national educational technology leader focused on evidence-based learning transformation. He formerly served as the director of the Office of Educational Technology at the U.S. Department of Education. In his role at the department, he was an adviser to the Secretary of Education and developed national educational technology policy, formed public-private partnerships to assist state and local education leaders in transitioning to digital learning, helped school districts expand the use of openly licensed educational resources (OERs), and collaborated with stakeholders to nurture a robust ecosystem of edtech entrepreneurs and innovators. He also worked on a cross-governmental team to bring high-speed broadband, interactive devices, professional development for educators and leaders, and high-quality affordable digital content to U.S. classrooms. He is a strong proponent of the active use of technology by learners. South has led learning product development teams at startups, museums, nonprofits, corporations and higher education institutions. He has also directed a host of learning programs and consulted on projects in China, Korea, Mexico, South America and the Middle East. He holds a doctorate in instructional psychology and technology from Brigham Young University.

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