The many ways in which AI can help teaching and learning

When you hear the term artificial intelligence (AI), the first thing that probably comes to mind is a science fiction movie or novel set in a distant future with robots and computer systems that make decisions on their own. However, the use of artificial intelligence today is so common and subtle that sometimes we do not perceive or identify that it is behind some of the daily activities we perform or the applications we use.

On our cell phones alone, we have within our reach apps that make use of behavioural algorithms that make recommendations of products, photographs, music and videos, as well as the route we should follow to commute to work or the weather conditions we can expect in the following days.

Today artificial intelligence is routinely used in many industries such as financial services, insurance, retail, entertainment and many others.

In November 2019, Tecnológico de Monterrey in Mexico created the Artificial Intelligence Hub, which coordinates research, academic and entrepreneurship activities related to the ethical use of artificial intelligence, with an emphasis on the highest social impact in areas such as health, education, access to water and preventing violence – some of the key Sustainable Development Goals.

A teaching and learning aid

One of the most promising and potentially beneficial uses of artificial intelligence is in education, and one of the most interesting uses is in the teaching-learning process.

Starting in 2019, Tecnológico de Monterrey began the deployment of an educational technology ecosystem for our Tec21 model for undergraduate students.

This educational model aims to address challenges in the development of transversal and disciplinary competencies, and one of its main components is the incorporation of cutting-edge technologies, such as artificial intelligence, which allows the monitoring of and supports the development of student competencies.

To enable this challenging educational model, we designed an ecosystem where one of the main components is analytics for the personalisation of our students’ learning. With this in mind, each component of this educational technology ecosystem is connected to our institutional data strategy.

As a result, today we have a large amount of data about the teaching and learning process that allows us to present students, professors and leaders with relevant information for academic decision-making.

For example, today our students have a dashboard to track the development of their competencies, and in the next phase the idea is to personalise their learning experience according to the courses they are taking, the learning resources they need according to the development of their competencies and the software resources or bibliography they require according to the courses they are taking.

In addition, various technologies are used for automation and personalisation, which will save academic staff time in the long run as they seek to find what works better for specific students.

In the case of adaptive learning, instead of the professor offering a continuous series of explanations to students to help them understand a concept, the student can, through a personalised path generated by artificial intelligence and machine learning, access interactive content as many times as necessary to understand the concept and access resources that the analytics have detected that he or she requires.

Academic integrity

An issue that became more relevant during the pandemic was to ensure the academic integrity of online exams. Technology is used to help the professor achieve consistency and standardisation of conditions during the evaluation process.

With the use of artificial intelligence, the video from the student’s webcam is analysed during the exam, automating the identification of the student and detecting, if necessary, relevant moments in which the academic integrity of the evaluation was put at risk.

As part of ensuring academic integrity, using the ability to analyse text with artificial intelligence, each paper submitted by a student is reviewed to determine if it is authentic or if it contains sections similar to those found in other papers submitted by students in the same group or from the same institution.

This analysis capability can be extended with the automatic validation of private or public data sources on the internet and made available to the professor in an analysis report.

The role of analytics

Learning analytics are based on data analysis and the use of models and-or behavioural patterns that, supported by AI, can provide recommendations, profiling and-or personalised learning paths. These analytics help students to identify success factors, risks in the achievement of sub-competencies and progress mapping with respect to their courses.

In the case of professors, analytics are a tool that can identify the courses in which students require support to achieve the development of sub-competencies as well as sub-competencies that require more attention.

A project currently under development determines in real time the cognitive-affective states of students during different moments in class using facial analysis, measuring factors such as emotional states, affection and activation. This is used to support teaching in the classroom, for both online and hybrid formats.

The objective is to offer the professor a real-time dashboard that allows instant adjustments to be made to the class dynamics by varying stimuli and didactic strategies to increase students’ predisposition to learning. In addition, an integrated report will be generated to provide academic communities with details of the chronological evolution of the cognitive-affective state of students in order to improve the pedagogical redesign of course activities.

A subtle tool

Artificial intelligence has become an everyday and subtle tool used in various areas, including education. In education it can offer significant benefits for learning and skills development, as well as improve efficiency and integrity in assessment.

Tecnológico de Monterrey will continue using artificial intelligence to boost learning. In the end, the objective is simple: to use artificial intelligence in a way that helps our professors and students to make our educational model more efficient and effective.

Bertha Alicia Saldívar Barboza is Educational Technology Director at the Tecnológico de Monterrey, Mexico. Sadie Guerrero is Edtech Solutions director, Irving Hidrogo is Director of Innovation with Emerging Technologies and Enrique Cortés is AI Hub Director.