As artificial intelligence opens up new possibilities in education, districts leaders are getting more insight into how machine learning will enhance teaching.
Artificial intelligence will power more complex and collaborative learning activities along with deeper assessments of students’ knowledge, according to “AI and the Future of Learning,” a report from Digital Promise and the Center for Integrative Research in Computing and Learning Sciences.
So far, AI has often been imagined as an individual user interacting with a single device. The report, which collected ideas from 22 experts, promises AI-powered social learning that would support groups of students working together with a range of resources.
Such AI systems are socially aware, and can use social interaction to support students’ academic performance.
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As for assessment, an AI agent could help a teacher assess their writing by incorporating the students’ strengths and weaknesses.
Such technology could also provide suggestions for improvement that considers all the contexts in which the student writes.
In the report, the panel of experts detailed seven areas of advancement that are needed to realize AI’s potential for teaching and learning:
1. Investigate AI designs to expand learning scenarios: The technology has growing potential to power open-ended science inquiry environments, social studies simulation tools and design thinking curriculum.
2. Develop AI systems that assist teachers and improve teaching: Experts recognized that many educational AI systems are student-oriented. They called for development of AI tools that supporting teachers in designing classroom experiences and as a professional development tool.
3. Expand research on AI for assessments: Though AI is already has been used to assess writing, science and math, its potential to measure learning has not yet been fully tapped. The experts said a new generation of analytic processes, including psychometrics, are needed to ensure that AI assessments are reliable, valid, and fair.
4. Accelerate development of human-centered or “responsible” AI: There is need for AI that serves learners with disabilities and accommodates learner variability.
5. Develop stronger policies for ethics and equity: Core standards, guidelines and policies must be for set effective, equitable and ethical practices in emerging AI. Policies and must be transparent so that educators can hold developers accountable.
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6. Involve education policymakers and practitioners: Toto test and evaluate emerging AI, educators will need to increase their awareness of the technology’s risks and barriers. The experts suggested that educators and districts may also need incentives to get involved in AI evaluation and policies.
7. Strengthen the overall AI and education ecosystem: Shaping AI for educational good will require strong ecosystems of educational leaders, innovators, researchers, industry leaders, start-up companies, and other stakeholders. This includes rigorous requirements for sharing data.
