Adoption of artificial intelligence is on the rise: According to research firm Gartner, 37 percent of organizations have now “implemented AI in some form,” and adoption is up 270 percent over the past four years.
Schools are following suit: Technavio’s “Artificial Intelligence Market in the US Education Sector 2018-2022” report predicts a nearly 48 percent growth rate for AI tools over the next three years.
The key to AI success is specificity. It is crucial to define key needs AI tools can meet and shortcomings it can address. This is especially true for K–12 institutions faced with limited time and budgets.
5 Roles AI Solutions Have in K–12 Classrooms
There’s no rulebook for deploying artificial intelligence in schools. While solutions such as IBM’s Watson Education and Google’s recently announced Google Cloud Platform storage and AI initiative offers the potential for schools to deliver personalized learning strategies and offer analytics-based performance insight; how K–12 institutions leverage these tools is entirely up to them.
Some of the most common concerns are around human bias and overall accuracy. As RAND senior policy researcher Robert Murphy notes in a post for Education Week, “maybe 10 percent, 20 percent, 40 percent of the time [the system] will get it wrong,” making AI in schools an excellent supplemental tool, but no replacement for teachers.
As noted by Education Technology, the increasing use of AI in schools also brings ethical questions to the fore. Organizations must now consider what type of data is being collected, how this information is being used and what controls are in place to safeguard student privacy. In addition, administrators are worried about how increasing AI adoption will impact human staffing.
The best bet for schools adopting AI is to define how these solutions can drive positive outcomes before allowing them access to student data. While individual usage may vary, effective AI in education can be grouped into five general roles:
Automation: The simplest application of AI often provides the most immediate benefit: By automating straightforward tasks such as grading, digital asset categorization or timetable scheduling, educators can increase the amount of time they spend actively engaging with students.
Integration: AI solutions can integrate with other IT initiatives such as smart technology and IoT-driven networks to provide personalized learning solutions for students.
Acclimation: Technology is now an integral part of both educational and business environments. Recent Pew Research data shows that 95 percent of teens have access to a smartphone and 45 percent are online “almost constantly.” AI in schools can help acclimate students to the pace of technological change.
Delineation: Students’ needs and curriculum priorities are constantly shifting, making it difficult for educators to ensure the content they deliver remains relevant and actionable. AI-driven analytics in education can help spot critical trends and delineate key markers to help teachers design the most effective classroom experience and drive digital transformation.
Identification: Data analytics informed by adaptative AI solutions can help identify critical areas for student and teacher performance. Combined with robust security and access controls, AI can help spot and remedy potential problems in their formative stages.
How K–12 Schools Are Using AI Tools Now
So, what does this look like in practice?
In Florida’s Putnam County School District, educators are leveraging new content monitoring software to both automate the process of flagging potentially sensitive internet searches and add critical context to flagged requests.
And in New Jersey, Slackwood Elementary School is using an AI-assisted teaching assistant called Happy Numbers to identify where students are struggling with math benchmarks and provide personalized assistance.
Solutions such as the Presentation Translator — a free PowerPoint plug-in — provide real-time integration of multilanguage subtitles to help students better understand instructions in class or provide remote access for those dealing with illness or other family concerns.
Put simply, the current climate of AI in schools one of hopeful hesitation. While technology is rapidly advancing, educators are taking a step-by-step approach to ensure the solutions they adopt deliver specific outcomes to address existing needs.
What Does the Future Hold for AI in the Classroom?
As artificial intelligence solutions inch ever closer to replicating the basics of authentic human thought patterns, what’s the impact for education? Broadly speaking, next steps for AI in school take three key forms:
Personalizing performance: Increased processing power and sophistication will empower AI solutions to better collect and extrapolate information, in turn helping educators create personalized learning plans for each student. New solutions such as Brightspace Insights are designed to capture, aggregate and analyze data from multiple sources, allowing teachers to gain student insight “based on the entire ecosystem of learning tools” according to Nick Oddson, senior vice president of product development for D2L, the creator of Brightspace.
Breaking bias: Human bias remains a stumbling block in education — and, as noted above, is also an emerging concern for AI tools. The future of AI in schools will leverage solutions capable of grading papers and evaluating exams using established rubrics and benchmarks to both automate completion and eliminate bias.
Aggregating assistance: Educators are typically masters of their craft; many have multiple degrees and often specialize in niche areas of student development and performance. The problem is that necessary administrative work often frustrates teacher efforts to engage with students. Here, the future of classroom intelligence takes the form of AI-driven assistants that deliver essential data to help teachers do what they do best: connect with students.
Schools Must Balance AI Effectively with Security
AI in education has reached a tipping point. Adoption is on the rise, but concerns around bias, privacy and human-machine partnerships necessitate a methodical and measured approach.
Current applications leverage machine learning and real-time data integration, but the future of AI depends on broader applications capable of adaptively applying practical intelligence to empower the critical emotional connections that drive student performance.
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