Learning Applications

As AI adapts to the changes in the landscape impacting what the learning experience is for students, it will naturally impact the teacher’s learning experience design work as well. Platforms will offer real-time and longitudinal feedback on levels of student engagement, notice when classroom assignments did not sufficiently address standards or learning targets, the teacher’s performance in designing and delivering instructional activities, recommending related content, and eventually support teachers to choose and adapt content to better respond to students’ interests, desires, needs, and community assets.

At St. Vrain School District, education leaders are integrating new forms of professional learning to rapidly learn nd implement AI. They are calling it Exploration AI and it encompasses:

• Self-Directed, Gamified Learning: Throughout the year, educators in St. Vrain Valley Schools engage in an AI-centric Bingo game. This encourages them to experiment with a range of AI tools in scenarios like designing a weekend getaway, formulating a personal workout regimen, or crafting innovative formative assessment strategies aligned with student learning outcomes.
• EdCamp Pop-Ups: Diverging from conventional professional development paradigms, our educators can participate in monthly sessions that emphasize collaborative dialogue and shared learning, demonstrations, and hands-on experimentation designed to enhance their pedagogical tactics and their understanding of AI’s myriad applications.
• School Champions: Each school will identify an “Exploration AI” champion, who will collaborate with the school’s leadership team to interweave AI strategies into their site-based monthly late-start professional development days. These champions, likely AI pioneers themselves, will invigorate and inspire the broader school community with their passion and knowledge.

Examples of technology solutions:

• Cognii: Personalized learning models through AI
• FACT: AI orchestration system
• Mursion: Orchestration and performance monitor
• Edthena AI Coach: a virtual assistant to monitor teaching and provide suggestions and feedback about improvement opportunities
• Osmo: Altering learning experiences
• Packback: Automated moderation of discussions
• Nolej: ai curriculum creation
• Waggle: AI co-pilot for management and team improvement

Adaptive learning solutions have been assisting with differentiated instruction for educators by enabling them to personalize instruction. When used well, this technology can result in a curriculum that is flexible enough to meet the needs of each student no matter where they are in their learning journey. In fact, tools like Knomadix view themselves as an “operating system” rather than an add-on. This approach makes it easier for students to interact as it removes pressure from prompting. Instead, the AI is automatically responsive and adaptive, resulting in higher-quality learning. 

The way this software functions is most familiar to educators who have employed a diagnostic tool for all their students such as the NWEA MAP assessment suite or a credit-recovery program from a vendor such as Edmentum. With constraints defined by input from the program administrators, these tools employ, “…[c]ommon machine learning techniques include decision trees, support vector machines, and ensemble methods,” as defined by MathWorks.com. Increasingly challenging items are placed before the student until the edge of their learning is discovered and then items or activities nudge growth from this point forward.

In a recent Getting Smart blog by Jeremy Roschelle & Pati Ruiz, the authors shared that “Another example, from [the] Center for Integrative Research in Computing and Learning Sciences (CIRCLS) research hub, is a National Science Foundation-funded project that is using AI technologies to develop new accessibility tools to enhance programming experiences for people with blindness or visual impairments (BVI). Teaching programming and data science skills to students with BVI is challenging without AI tools, due to the limited capabilities of current screen readers to properly read code. There are often complications when using screen readers to navigate lines of code to locate the desired line when programming. The success of this project will improve the adaptivity of teaching computer programming and data science to students with BVI and may improve access to career opportunities.”