Artificial Intelligence Goes to School
Listen and learn : Panel
Monday, June 24, 11:30 am–12:30 pm
Location: Room 124
Leah Aiwohi Hina Baloch Nancye Blair Black Janice Conger Dr. April DeGennaro Dr. Yolanda Ramos Dr. Joseph South Matthew Ybarra
ISTE and General Motors Corporate Giving are collaborating in a pilot program that cultivates future artificial intelligence 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.
|Audience:||Coaches, Curriculum/district specialists, Teachers|
|Attendee devices:||Devices not needed|
|Focus:||Digital age teaching & learning|
|Topic:||Computer science and computational thinking|
|Subject area:||STEM/STEAM, Computer science|
|ISTE Standards:||For Students:
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.
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.
Janice is a middle school librarian with a passion for technology and making. She is a member of the Pennsylvania School Librarian's Association's conference committee and serves on the southeast regional board of ISTE Affiliate PAECT. She is part of the Mini Regional Leadership Academy sponsored by PSLA and the University of Pittsburgh. She leads professional development in her district and presents her library successes and struggles at PETE&C, the PSLA conference, and at ISTE. She is a member of the ISTE Librarians Network and is part of the ISTE Librarian's Playground!
ISTE senior regional director for the Americas, leads the development and execution of ISTE’s Americas regional strategy, including the design, development and implementation of sustainable engagement opportunities across the region. She has significant experience in designing and building solutions, strategic relationships and executing collaborative partnerships in the corporate, nongovernmental organizations and public sector. Yolanda also has worked with K-12, higher ed and corporate sectors in the U.S. and with Ministries of Education in Latin America and the West Indies.
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