What is Artificial Intelligence? According to Wikipedia, Artificial intelligence is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals.
Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points, and draw conclusions. They can reason, observe, and plan. (Understanding AI technologies and how they lead to smart applications)
If you want to view a few experts discuss AI then Ted Talks offers some interesting thoughts and perspectives on their thoughts on future with AI https://www.ted.com/playlists/310/talks_on_artificial_intelligen
What does this mean for learning?
Well fortunately the Center for Integrative Research in Computing and Learning Sciences (CIRCLS)
recently convened a panel of 22 experts in AI and in learning to discuss these issues. They met digitally for seven hours over a two day period in a facilitated process with different topics and breakout formats. They considered the following two broad questions:
- What will educational leaders need to know about AI in support of student learning in order to have a stronger voice in the future of learning, to plan for the future, and to make informed decisions?
- What do researchers need to tackle beyond the ordinary to generate the knowledge and information necessary for sharing AI in learning for the good?
Download the Report
Who are the experts?
The 22 member panel was Impressive, to say the least, and I’d like to spend some time discussing being digital with several of them.
The 22 experts on the panel are listed below:
- Dr. Russell Almond, Florida State University;
- Dr. Ryan Baker, University of Pennsylvania;
- Dr. Avron Barr, IEEE;
- Dr. Gautam Biswas, Vanderbilt University;
- Dr. Justine Cassell, Carnegie Mellon University;
- John Cherniavsky, National Science Foundation;
- Dr. Sherice Clarke, University of California, San Diego;
- Dr. Chris Dede, Harvard Graduate School of Education;
- Dr. Sidney D’Mello, University of Colorado, Boulder;
- Dr. Janice Gobert, Rutgers Graduate School of Education and Apprendi’s;
- Dr. Cindy Hmelo-Silver, University of Indiana;
- Dr. Susanne Lajoie, McGill University;
- Dr. Diane Litman, Dr. Rose Lucklin, University College London, Knowledge Lab;
- Dr. Maja Mataric, University of Southern California;
- Dr. Danielle McNamara, Arizona State University;
- Dr. Jaclyn Ocumpaugh, University of Pennsylvania;
- Dr. Amy Ogan, Carnegie Mellon University, Human-Computer Interaction Institute;
- Dr. Zach Pardos, University of California, Berkeley;
- Dr. Brian Smith, Drexel University;
- Dr. Kurt Van Lehn, Arizona State University;
- Dr. Marcelo Worsley, Northwestern University.
Other participants included:
- Kevin Johnstun, U.S. Department of Education;
- Sara Trettin, U.S. Department of Education;
- Adam Safir, U.S. Department of Education;
- Christina Chhin, U.S. Department of Education;
- Edward Metz, U.S. Department of Education.
About the Report
This report introduces three layers to frame the meaning of AI for educators.
First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths.
Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. These specific capabilities can be engineered into solutions to support learners with varied strengths and needs, such as allowing students to use handwriting, gestures, or speech as input in addition to more traditional keyboard and pointer input.
Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion the most impactful uses of AI in education have not yet been invented. The report enumerates the important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning.
Through discussions among experts about these layers, we observed new design concepts for using AI in learning. Experts discussed how AI could support learning in terms of orchestrating complex learning activities with multiple people and resources, augmenting human abilities in learning contexts, expanding naturalistic interactions among learners and with artificial agents, broadening the competencies that can be assessed, and revealing learning connections that are not easily visible. These approaches go beyond familiar design concepts for individualized, personalized, or adaptive learning. To bring these approaches to life, experts suggested two broad scenarios.
Bringing The Layers to Life
The first broad scenario is called the learning environment notably featuring social learning. In this scenario, AI supports the orchestration of the multiple types of activities, learning partners, and interaction patterns that can enrich a classroom. This is different from many older images of AI that focus on an isolated individual who interacts only through and with a single device. An AI agent could provide support to a group of students as they work on a project or assignment together. This could include support for students to work as team members (e.g., noticing, listening to, and building on each other’s contributions) as well as task supports that help them organize, manage, and connect their contributions to an overall group goal. It could adapt to what groups need to work well together, as well as how groups transition between individual work, small group work, and discussions with the whole classroom. This kind of AI system is socially aware and can use social interaction with students as a way of bootstrapping their academic performance.
The second broad scenario assessment envisions going beyond what today’s assessments can measure. Instead of just grading an essay, an AI agent could help a teacher build a portrait of a student’s competencies. For example, AI can assess their writing, including students’ strengths and weaknesses, and provide suggestions for improvement that considers all the contexts in which the student does the writing. Teachers could, in turn, showcase other features of students’ writing experiences and accomplishments and build on these in instruction. This is fundamentally different from assessment metaphors today that focus on automated grading of a particular performance or diagnosing specific student errors. With added information about the student, the AI system can provide advice on how to support the learning process of that complete individual, based not only on their narrow test and class performance for a given class, but on their holistic background and trajectory.
The two scenarios connect. In a few years, it might become possible to connect learning with assessment as students collaborate to learn and learn to collaborate. A range of social, emotional, and cognitive skills might be better supported, going beyond the academic content typically measured today. Further, a focus on collaborative learning is just one way in which AI could enable powerful learning that aligns with how our society and its work are evolving.
We also noticed, in terms of risks, the experts were concerned with well-known risks related to data—such as privacy, security, bias, transparency, and fairness. But they also went beyond these expected concerns to talk about design risks and how poor design practices could unintentionally harm classes of users. In addition, they foresaw a major risk in not informing and involving educational policymakers and practitioners early and deeply enough.
7 Research Priorities
The panel made seven recommendations for research priorities:
- Investigate AI Designs for an Expanded Range of Learning Scenarios
- Develop AI Systems that Assist Teachers and Improve Teaching
- Intensify and Expand Research on AI for Assessment of Learning
- Accelerate Development of Human-Centered or Responsible AI
- Develop Stronger Policies for Ethics and Equity
- Inform and Involve Educational Policy Makers and Practitioners
- Strengthen the Overall AI and Education Ecosystem
Further Industry Discussions
The expert panel was well aware this meeting had limited involvement of other stakeholders and wanted to make clear this was an initial discussion. They noted the involvement of practitioners, policymakers, innovators, and industry in further discussions on these issues is imperative and the experts would gladly participate with a broader set of stakeholders.