Type of educational technology that utilizes insights from neuroscience and adaptive learning algorithms to tailor instruction and content delivery to the individual learner's cognitive processes and learning patterns.

Acquiring cognitive and emotional engagement data during the lessons can provide :

• Real-time feedbacks to the teacher, for maximizing class engagement, and 

• Student engagement trends over the time that can be used for academic program adaptation to individuals or to the whole class.

AI  can play a crucial role in processing and analyzing neurofeedback data, enabling the system to adjust content delivery based on the learner's cognitive responses. 

Machine learning algorithms can identify patterns in brain activity to optimize educational strategies.

Gamification is a leading trend in the neuroeducation market, leveraging game design elements to create engaging educational experiences (AlterLearning, 2023).

Integrating the principles from cognitive neuroscience when designing educational games can help in areas around cognitive processing and promoting engagement. 

One area researchers have looked at is intrinsic motivation and how that can be used in games in education to help learners. Neuroscientific research indicates that achieving a goal or reward in games can lead to the release of dopamine, a neurotransmitter associated with pleasure, motivation, and learning.

 By tapping into the brain's reward systems and natural motivation mechanism, gamification strategies such as points, badges, and leaderboards captivate learners' attention and sustain their motivation to engage with learning materials. Students are given opportunities to explore, experiment and problem solve and this paired with gamification strategies ensure students learn because they want to not because they have to.

Prodigy uses principles of cognitive science in the way it presents and practices math problems as well as the game design elements. 

Designing an environment where students are can create, explore and live out a storyline while solving math creates an environment of interest and curiosity. The games feedback mechanism and adaptive difficulty levels also support skill development and mastery through practice. 

The game design features like immersive storytelling, customization of characters, rewards and levelling up appeals to student's interests and preference while also tapping into learners intrinsic motivation which will help them to continue to try and progress as the math becomes more challenging

Duolingo has a team of scientists and designers who work on the platform including Dr. Ben Reuveni who has a Ph.D in the cognitive science of learning and memory. Duolingo is built on many principles of neuroscience including spaced repetition, contextualized practice, and immediate feedback, while the game-like activities engage multiple cognitive processes, such as auditory processing, vocabulary acquisition, and grammar comprehension.  These bite-sized lessons, gamified challenges, and progress tracking enhances motivation and promote continuous engagement.

There is a lot of potential to enhance educational experience when you start to look at the the integration of artificial intelligence (AI) into neuroscience-informed gaming. 

• Personalization: AI can be used to analyze learners data to adjust and personalize the learners content allowing for an individualized learning experience. 

• Assessment and Feedback: AI can be used to give feedback and provide insight into the learners progress which can help them continue to learn and advance. 

• Social Interaction: AI facilitates collaboration and social learning within gaming communities, enhancing engagement and motivation.

Brain Computer interfaces aim to decode measurements of an individual, typically electroencephalography (EEG) signals (aka brain waves), supplemented with other data such as heart rate, eye movement, etc.

The goal is to develop an understanding of an internal state of the user’s mind: emotions, intentions or thoughts. Application potentials of these are profound.

Significant interest in the field is in the development of adaptive technologies. For individuals with impaired use of limbs or needing prosthetics, the ability to read movement intentions from the brain offers the potential of recreating complex movements in prosthetics or other aids.

Feedback on mental states can also be used to monitor a user’s stress and cognitive load. These aspects have significance in multiple fields, including education. Being able to subtly track when students are focused, distracted, engaged or stressed, allows for sensitive adjustments to the learning process. Rather than timing lessons or breaks based on schedules or signs of disruptive behavior, teachers could adjust based on how well individual students are proceeding, intervening to support when needed or changing the task when a break is justified.

While this study is small (20 participants), similar systems have been developed and tested previously. So far, research supports the approach as having beneficial effects for ADHD symptoms. This indicates that this approach may be a viable alternative to medication for ADHD, and possibly other conditions.

The program is significant in the area of BCI development as it is simple and self-contained to be used at home without a specialist technician. While this program was rolled out to patients already diagnosed with ADHD and undergoing treatment, the SenzeBand is available freely and can be adapted to other applications. With the general availability of such products, the potential exists for BCI based applications to target many other purposes.

The data collected can be used to determine a range of mental states (stress, fatigue, focus), but also specific muscle stimulus. This allows not only general feedback on the user’s activity, but also specific input control, such as operating a drone.

To encourage the ethical use and development of these devices, the Galea software supports open-source development of applications. In essence, the data used by its applications is available for users to adapt into new applications, allowing focus on uses in specific areas or complex individual needs.

AI plays an important role in BCI development, as the complexity of mental states and actions is matched by the imprecision and noise of available data. Careful use of AI can bridge these, making real time deductions that would be impossible otherwise.

A significant demonstration of this is using AI to create speech from an individual’s brain activity. Utilizing various approaches, including large language models like GPT-1, EEG data can be compiled into intention and meaningful sentences, with a text to voice synthesis create an output. This can allow individuals with certain speech disabilities to talk again.

Other research points to greater potential applications in understanding thoughts beyond linguistics. In a recent study, researchers were able to use data from brain measurements to recreate the sounds that the user was listening to (in this case, a Pink Floyd song). While the recreation is far from perfect, features of the song can clearly be recognized.

Cognitive enhancements aim to increase an individual’s mental performance in a general sense, and are not tied to specific subjects or content ideas. This could include improvements in memory, focus, problem solving skills, etc.

An important element of this is that the term is usually meant to describe improvements for healthy individuals, as opposed to treatment techniques for mental and/or physical illness.

Existing methods of cognitive enhancement include drugs (caffeine, Ritalin) or behavioral techniques (mediation, use of mnemonics). New potentials exist for the use of technology in this field, such as brain stimulation via electric current.

A hybrid approach exists by using software applications to develop abilities. These are modern extensions of behavioral approaches but add an external guidance and feedback loop, instead of putting all tasks on the user.

At present, these procedures are being used to treat a variety of conditions, including Parkinson’s disease and depression. Proposed near future applications include treating addiction, Alzheimer’s and other neurological diseases.

Brain stimulation faces the challenge of specialized equipment for the procedure. Current technology is unlikely to be used beyond serious therapeutic contexts except for research purposes. However, the development of more refined, safer, and low cost, non-invasive technology will likely change this. 

Despite the lack of hard evidence of long-term sustained improvements, Lumosity and other cognitive training apps have not been discredited either. There is some research to support the claims, and recommendation for using cognitive training to treat mild cognitive impairment has been made by the American Neurological Association.

Even with more realistic phrasing on its outcomes and mixed research support, Lumosity has remained a viable and well used app. As of 2022, the company claims over 100 million active users.

The adoption of cognitive enhancement methods beyond treatment settings will depend on the development of effective and safe technologies. Approaches such as direct brain stimulation could benefit from this  tremendously, using AI tools to decode from minimal and noisy sensor data to determine optimum magnetic or electrical signals. With a more accurate target determined, smaller signals and less invasive devices will result.

Brain training apps remain something of an ideal in this space, with the potential to reach a wide audience without dedicated equipment or support specialists. To achieve more effective results, AI may be used to deduce specific strengths and areas of concern for individuals, in a much more sophisticated way than is done now. This would then lead to highly individualized training tasks and programs.