Recently I posted the 12 most intriguing problems educational leaders are facing this year moving forward. One of the more significant issues for leaders to meet asks “What is the future bringing to schools?” To answer that question, last week I wrote briefly about the coming influence of AI (artificial intelligence) and adaptive learning on education.

I also wrote about one of the significant implications of this technology which will become how AI will allow more blended and personalized forms of learning.  As suggested in our last blog, AI will increasingly blur the line between formal, informal, home, school and self-paced learning formats. This theme has vast implications for how schools are structured and how schools assess and track the learning of students.

AI is becoming a significant driver in our society and future and will, and already has to some degree, impact layers of our educational system down to the learner level.  This blog will take us to the year 2030 where one school has begun using the power of AI and adaptive platforms to help us glance into a possible future.

The Start of the Year

It was the year 2030, and Tony Mendiola had received his acceptance letter to Adaptive High School, a high school that had recently reconfigured around the premise of adaptive learning. As he made his way from the parking lot to the entrance, Tony wondered how much different this school would be from his previous K-8 school.

Tony’s previous K-8 school had been a good experience for him because it used lots of blended learning which he enjoyed, but his learning in some areas had not been as rigorous or deep due to the time constraints put on him. His parents knew that while he liked math, it sometimes took him longer to grasp a concept which caused him anxiety. They were looking for a school that allowed different pacing and lots of extra support. He also knew he had developed some bad habits. While his school talked a lot about developing his character and personal skills, nothing formal had been apparent. He got away with C’s because that was allowed.

As he entered the door to a modern building that didn’t look like a school, he remembered the email that said to plan on being there all day for onboarding whatever that meant. Tony was greeted by a student guide who took him to Mr. Brewer’s room who would be his learning advisor through his time at AHS.

As Tony walked in, a retinal scanner took a scan. He was asked to open a new laptop that automatically scanned his retina as his password, and his screen opened up to the AHS adaptive learning management system. Mr. Brewer took a few minutes to introduce himself, and then explained that today students would be doing a round of initial assessments to create a personal learning path for each student.

Tony thought this wouldn’t take very long, but he took assessments in math, coding, informational reading, writing and science given as an integrated problem. He was also given personality and strength inventories along with interest inventories. As Tony worked through these assessments, the laptop scanned all of Tony’s physical signs, and the adaptive assessment program noted all of his movements per question including how long it took, how often he used hints, and what kinds of resources he naturally used.

In addition to the regular assessment, Tony choose one of the ten difficult problem- solving tasks to solve. For this task, Tony was given a baseball hat to wear that plugged into his laptop. This hat had many sensors that measured his engagement level, the capacity of his short-term memory, how efficiently he pulled knowledge from certain parts of his brain and how he progressed through the problem. It also flagged emotional changes as some areas turned different colors when he was disengaged or when frustrated.  At the end, he answered specific reflection questions which he recorded through the programs recording device. This recording then analyzed his responses with artificial intelligence.

For the last assessment of the day, Tony and three other new students moved to a room that had motion sensors and recording devices. The team saw a box of materials and directions to build a small robot that could run through an obstacle course, find a penny and flip it back to the team. They were given two hours to do this task, and every interaction, trial, and syllable spoken was recorded and analyzed by artificial intelligence to see how each team member compared to the team skills required by local industries.

As Tony headed out the door to meet his mom, all of the data from the day had already been analyzed and a path through the next 90 days was formulated and ready for him the next day.

Day 2 Onboarding

During Day 2, Tony was required to have his parents attend with him to help develop and review his personalized learning plan. Tony and his parents first met with Mr. Brewer to discuss his academic assessments and his learning plan. For all academics, AHS used a competency-based system based on the state’s newly enhanced standards. Each competency used a 1-10 scale so parents could easily understand with level 3 the incoming expectation for freshmen, and level 10 the expected for career and college ready. Students who reached level 10 in all major competencies were considered “ready” and could leave AHS.

As Tony thought, he was at a 3 in most areas, a 5 in coding, but still a 2 in mathematics showing he had difficulties with proportional reasoning and understanding of fractions.  His assessments also confirmed that Tony tended to disengage when problems became more difficult, and he often relied on other resources when stuck. His engagement levels showed an up and down pattern showing Tony had trouble maintaining his attention, especially when frustrated.

His team skills showed that Tony was far from career ready as he did not assert himself during conflict, and did not speak up during the problem-solving session. In Team skills, Tony rated at a 1 level. All of these lower areas in self-regulation and team skills became goals that Tony would work on in school at home and in his other activities

During this meeting, Mr. Brewer discussed the passion/career courses Tony could take. His interest inventories and strengths suggested Tony would benefit from more programming classes but also art and design.

As his parents talked more with Mr. Brewer, Tony took a guided tour of AHS’s adaptive learning management system. This system on Tony’s laptop would be his portal to everything at the school including his independent/academic time, his team project time, and his passion/career time. It was available 24 hours a day.

This platform had been created by AHS in conjunction with PLtech, a local tech company and utilized the latest in biological markers, engagement markers, and artificial intelligence for adaptive learning. This platform was state of the art in blended and personalized learning. Tony’s teachers could get over 100 different by the minute indicators to know when they needed to intervene.

Tony’s dashboard did not have courses listed but had large areas and competencies labeled by the developmental level. For instance, his coding started at level 5 while his writing started at level 3. His math level began at Level 2 as a brief acceleration guided by highly researched teaching resources. As Tony toured around the platform (done by more artificial intelligence), he noticed that each competency had a variety of resources he could use and not only quizzes by higher level tasks that would be scored by the computer. The platform also had a chat feature Tony could use to access his teachers when needed- even after school hours.  All answers that Tony entered were analyzed immediately, and other resources were brought into play to adapt to the student need either up or down including notifications sent to teachers.

Two progress bars showed on top for each area: one for progress toward the competency, and one for engagement level. If the engagement level dropped below 80, the LMS would give behavioral nudges to get Tony back on track. If the level fell below 50, an alert was sent to a learning coach who would meet with Tony to get him on track. Weekly, engagement and progress measures were reviewed by teachers to determine who needed a more scaffolded set of supports so students would not get too far behind. As part of a state pilot program, AHS was actually funded by levels of engagement and competency completion- a first in the state.

The other interesting thing Tony noticed was that the LMS always opened up to what was known as the SRL screen (self-regulated learner). This page would remind Tony of his overall 90 day goals and display his engagement and performance data from the previous day.

At the start of each day,  Tony would be asked to set a daily learning plan of how much he wanted to get accomplished and what strategies (learning and self-regulation and motivation) he would use and pay attention to during the day. At various time during the day, a screen would pop up and have Tony rate his use of the chosen strategies and if he needed to make any adjustments. In a sense, it acted as a second brain to show Tony and all students at the school how to become self-regulated which was an elemental design feature of AHS.

At day’s end, the SRL screen would pop back up and ask Tony to reflect on his progress and use of strategies. Over time as this habit became embedded, the SRL screen would become less frequent, although there were some Juniors who still had to use the SRL screen quite a bit.


Too far out there or just on the horizon? While adaptive assessments and tools exist today, no company yet has created an LMS that combines adaptive assessment, adaptive curriculum, and resources while teaching students how to self-regulate their learning and grow these essential skills.

We know that the integration of all of these areas is critical yet very difficult for teachers to manage on their own. As AI becomes more prevalent in our society and schools, can we truly blend the learning experience so teachers can do what they do best, and let technology be a significant tool to help? Only time will tell, but we need a strong vision of how AI can be used to help all students master essential outcomes.