Digital transformation brings a huge societal transformation and requires each person to be well equipped with strong digital skills. Educational Robotics (ER) is a powerful tool that helps develop the digital literacy. However, this technology can do much more than foster digital skills. In fact, by combining the computational power of ER kits with the long-standing achievements in the field of Educational Data Mining and Learning Analytics, the challenge of assessing learners’ behaviors and performances during ER activities is finally addressed. The independent functional blocks of ER kits can track and report learners’ interactions with the kit during the exercise. Interactions can be analyzed by means of adequate AI algorithms and then visually presented to the teacher, educator, trainer or researcher.

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The educational robot market is projected to grow from USD 1.3 billion in 2021 to USD 2.6 billion by 2026.

Based on application, the educational robot market is segmented into social/socially assistive robots (usually humanoid ones) and non-humanoid robots. Smart Blocks will address mainly (but not exclusively) non-humanoid DIY kits, which are meant to assist learners in the design and programming of a robot, as well as to enhance their problem-solving skills.

Since there is a wide availability of tools in the educational robot market, the first step of our patented methodology will be to first release an add-on for all the most common educational kits on the market offering a wealth of resources available as cloud services and learning dashboards. This first phase will reach mainly the European segment but held the potential to grew faster and reach other segments.

2.6 billion USD by 2026* Total Available Market
*source: Markets and Markets, SE6132, Jan 2021

40400+ teams
Only for the FLL**
Service Available Market
**source: Wikipedia, FIRST Lego League Challenge

927.73 million
by 2028***
Share of Market
***source: Business Market Insights, TIPRE00024106, Aug 2021


Millions of STEM jobs are projected to go unfilled in the near future. Moreover, people and social systems are increasingly relying on technology to survive on this planet. Thus, the digital and technological competence are fundamental life skills. To meet the need for digital and technology education, smart pedagogies, innovative toolkits and assessments of the learning outcomes are needed. Despite the wide availability of toolkits on the market, there are only few references to learn how to effectively use technology in the classroom and even less instruments to help educators assess the outcomes of learning process. This gap is due to the lack of non-invasive real time measurement systems that can collect data from real-world operative environment. State of the art techniques rely on the experience of educators or on paper tests and questionnaires. The drawback of such techniques is that they need time and collaboration to collect data from the field, so a real-time measurement is not possible. The lack of real time measures of the process makes it impossible to realize a real time monitoring system that fully support educators in understanding learners’ capabilities. Furthermore, the information analysis function regarding the activation of the individual blocks (temporal or event based) and of the collected data, allows to reconstruct in real time the effectiveness of the educational process of which the devices themselves have been employed, in order to monitor and adjust the teaching.

Current Technology Limitations

Usually, educational robots come with instructions on how to assemble and program the kit, but they give no advice on how to assess the outcomes of the activity nor provide benchmarks on usual learning trajectories along important dimensions, like problem solving styles or computational thinking.  The lack of evaluation methods that can summarize learning along different dimensions, and complement the rich qualitative observational methods, is putting a constrain on the uptake of educational robot in the market. Moreover, some teachers and educators still perceive robots only as useful to engage learners, they feel that rethink their pedagogical approach to build a didactic plan around a robotic tool won’t necessarily result in better teaching and learning. Smart Blocks help assessing the output of the learning process both in education and in other field where learning with robots is possible. Thus, researchers could improve clinical results and large-scale experimentation, and educators can focus on building activities and providing feedback to students, because Smart Blocks can record, analyse and present the result of the activity while the activity is still being performed. The only possible comparison for Smart blocks at the moment is with the traditional approach to the evaluation (tests, quizzes…), or relying on external observer producing detailed reports of activities. Anyway, they are time consuming, non-real-time and invasive techniques.

To exploit the full potential of robotics in education a tool like Smart Blocks is needed, namely a validated, accurate, real-time measurement tool for smart open learning environment that shows the learning trajectory of the learner and advices the teacher when a feedback is needed to avoid learning failure. Currently, educational robots do not integrate such solution, nor a separate platform can guarantee the integration with a robotic toolkit. That’s why UNIVPM patented the solution and will prompt the creation of an academic spin off.

Killer Application

The products deriving from the patent will be a fast-deploying tool for teachers, educators and other trainers.

Costs for the developments of such line of products involves only basical elements, both software and hardware, and development. Currently, the  first system is at TRL4 and one of its applications can be found as scientific results in:

  1. Scaradozzi, L. Cesaretti, L. Screpanti and E. Mangina (2021), “Identification and Assessment of Educational Experiences: Utilizing Data Mining With Robotics,” in IEEE Robotics & Automation Magazine, doi: 10.1109/MRA.2021.3108942.

This innovative system is rapidly drawing the attention and many schools declared their interest in using it. This AI-enabled technology hold potential for influencing trends and sales on the market of training and education with robots. Moreover, it will support evidence-based education in the classroom, thus helping teachers integrating robotics into the design of their courses.

Our Technology and Solution

During educational robotics learner is engaged in building and programming a robot to solve meaningful challenges. Focus on the exploration activity of the task is very important and their attention should not be diverted. So, Smart Blocks will:

  • record real-time data from the interaction of a generic user with the educational robotics kit
  • allow the tracking of interactions of multiple users with different kits at the same time
  • analyze data with the most advanced ML algorithms to describe the learning trajectory along different dimensions
  • analyze multiple tracks at the same time
  • visualize in a real-time fashion the results of the descriptive and predictive analysis on a customizable dashboard

The innovative methodology patented with Smart Blocks relies on a mining algorithm that will record a track of all the basic functional software blocks of the kit on a local or remote storage memory. This track is the raw data of the measurement that will feed the online Machine learning algorithm and describes learner’s approach to the solution of a given task.

Smart Blocks will be deployable on the most renowned educational kits available on the market. The remote server will implement the most advanced security measures and will comply with the most recent privacy regulation. Real-time results of the analysis can be displayed on computers, tablets and smartphones.


First, the Smart Blocks infrastructure will bring the capability to monitor the learning trajectories of students in an educational robotics activity.

Therefore, the infrastructure will let teachers and professionals of education to support the learning process by means of the very same educational kit, which is turned into a “talking” element.

The whole system will act as a real-time interpreter of the learner’s learning status, without distracting learner’s attention from the actual process of exploration and fabrication.

Supporting the introduction of educational robots at school, the Smart Blocks system will drive a revolution in laboratory teaching. The increased use of educational robots will led to an increase on the hours dedicated to the contamination of basic literacy and digital and technological literacy.

Moreover, such a system will provide a tool for researchers and clinicians to assess variables of interest in innovative learning procedures involving robotics.


Step 1:

  • hardware setup: creation of a software and hardware add-on solution for specific task
  • server setup: development of a server for the educational IoT solution (sensing information and send it to a clous where the AI will process learners’ behaviors)

Step 2:

  • AI algorithms selection and tuning: mathematical models of the learning process will be drawn from the complex system theory to adequately address the challenge; both supervised (neural network) and non-supervised (neural network, k-means. Fuzzy k-means clustering) machine learning algorithms will be tested for accuracy and accountability.
  • Experimentation: a first validation phase in operative environments of a restricted area will bring information on the system (Accuracy definition, gathering common questions from the users, first dataset to start the benchmarking activity,…)

Step 3:

  • Extended experimentation: a second validation phase in operative conditions will take place nationwide to provide further, more generalizable, results
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