Digital Co-Regulation in School

The article by Christos Simos (2025) on managing intense behavioral outbursts in schools through innovative digital interventions offers a timely look at how technology is reshaping emotional and behavioral support in inclusive education. Drawing on research published between 2018 and 2025, the paper explores how tools like biofeedback wearables, AI‑driven emotional analytics, virtual and augmented reality (VR/AR), and teacher digital coaching systems can transform how schools respond to crises, particularly for students with emotional and behavioral disorders (EBD), ADHD, autism, or trauma histories.

This is new emerging research with novel insights, not because technology itself is new, but because of how it is being woven into relational, preventive, and inclusive approaches such as Positive Behavioral Interventions and Supports (PBIS), Social and Emotional Learning (SEL), and Universal Design for Learning (UDL). Instead of seeing behavior only as “misconduct” to be punished, it reframes outbursts as signals of unmet needs that can be understood and supported.

Within this ecosystem, biofeedback plays a particularly interesting role. Broadly, biofeedback refers to training where physiological signals such as heart rate variability (HRV), skin conductance, breathing, or even brain activity are measured and fed back to the learner in real time, so they can gradually learn to regulate these signals. Neurofeedback is a subtype of biofeedback that focuses specifically on brain activity, typically using EEG, allowing individuals to practice shifting their brain states toward patterns associated with calm focus, emotional control, or cognitive flexibility.

Simos’ review brings these ideas into the concrete world of classrooms: how wearables, VR calm rooms, and digital coaching tools can decrease the frequency and intensity of behavioral outbursts, strengthen self‑regulation skills, and offer teachers new ways to co‑regulate instead of simply control.

Methods

Rather than testing a single intervention, the article uses a narrative literature review to map what we currently know about digital interventions for behavioral outbursts and self‑regulation in schools. This approach was chosen because the field is moving quickly: new tools, platforms, and devices appear faster than traditional meta‑analyses can keep up. A narrative review makes it possible to integrate controlled trials, qualitative case studies, theoretical frameworks, and policy papers into a coherent picture of how technology is being used in real educational settings.

The author searched major databases such as Scopus, Web of Science, PsycINFO, ERIC, and ScienceDirect, and complemented this with open‑access repositories (for example MDPI, Frontiers, and Springer Open). Articles published between 2018 and 2025 were considered, with a focus on school‑based interventions targeting behavioral outbursts, emotional dysregulation, stress, impulsivity, or frustration tolerance. Only peer‑reviewed work with clear methodology was included.

From an initial pool of 1,242 records, 156 studies met the basic inclusion criteria after screening. Following full‑text review for relevance and methodological quality, 72 key publications were retained. These included experimental and quasi‑experimental studies, mixed‑methods projects, meta‑analyses, and conceptual and policy‑oriented work.

Crucially, the review only included studies where digital tools were used in relation to behavior, emotion, and inclusion in schools. Technologies examined included:

• Biofeedback wearables (for example devices measuring HRV, skin conductance, and temperature) and, in some cases, neurofeedback‑style training supporting self‑regulation.

• AI‑driven dashboards that integrate behavioral data, physiological signals, or SEL check‑ins to predict escalation and support early, preventive intervention.

• Immersive VR and AR systems such as virtual calm rooms, social simulations, and emotion‑focused scenarios for practising coping skills, empathy, and conflict resolution.

• Gamified social‑emotional learning (SEL) and self‑regulation apps that deliver breathing exercises, cognitive‑behavioral strategies, and mood tracking in a child‑friendly format.

• Teacher‑focused digital coaching and training platforms that provide feedback, scenarios, and guidance for classroom management and de‑escalation.

The analysis was organised using a thematic synthesis model with both inductive and deductive coding. PBIS, SEL, and UDL served as the main conceptual lenses, while inductive coding highlighted themes like digital co‑regulation, teacher emotional labour, and ethical and cultural challenges. This allowed the author to link concrete outcomes (like fewer behavioural incidents) with deeper mechanisms (such as improved emotional literacy and executive functioning).

Results

The review highlights four main result clusters: (a) behavioural outcomes and incident reduction, (b) emotional and cognitive regulation, (c) teacher efficacy and emotional labour, and (d) systemic, cultural, and ethical conditions for sustainable implementation.

Across multiple studies, technology‑enhanced supports embedded in PBIS frameworks were associated with substantial reductions in behavioural incidents. AI‑driven behaviour dashboards and data‑informed PBIS systems reported drops of roughly 30–60% in office disciplinary referrals, aggression, and classroom disruptions, particularly in inclusive classrooms with high proportions of students with EBD, ADHD, autism, or trauma histories. Wearable biofeedback devices measuring HRV, skin conductance, and movement helped detect early signs of dysregulation, allowing for preventive co‑regulation instead of reactive punishment.

Students who engaged in biofeedback‑based self‑regulation training and daily gamified SEL exercises showed improvements in frustration tolerance, delay of gratification, and adherence to classroom routines. These behavioural changes were often accompanied by better classroom climate, peer acceptance, and cooperative behaviour. The review links these improvements to motivational theories such as Self‑Determination Theory, where autonomy, competence, and relatedness increase intrinsic motivation to use self‑regulation skills.

On the emotional‑cognitive side, immersive VR and AR interventions created safe environments for practising emotional awareness, empathy, and conflict resolution. Meta‑analytic evidence cited in the review suggests strong effects of VR‑supported SEL on emotion recognition, inhibitory control, and empathy, especially in neurodivergent students. Parallel work in biofeedback and neurofeedback shows that repeated self‑regulation training can strengthen prefrontal circuits involved in emotional monitoring, inhibitory control, and cognitive flexibility, and is associated with improvements on tasks such as Stroop and Go/No‑Go.

Teacher‑focused digital tools also showed consistent benefits. AI dashboards, VR‑based co‑regulation simulations, and online coaching platforms increased teachers’ sense of efficacy, reduced emotional exhaustion, and helped reframe outbursts as communication of unmet needs rather than defiance. Professional development hours (often in the range of 10–20 hours of targeted digital‑SEL training) emerged as a key moderator of effectiveness.

At the same time, the review underlines systemic and ethical issues: unequal access to advanced tools across socio‑economic contexts, cultural misalignment between “default” emotion scripts and students’ lived experiences, and concerns around surveillance, data privacy, and algorithmic bias. These risks can be mitigated but not eliminated through ethics‑by‑design, transparent governance, participatory co‑design, and alignment with UDL principles (for example adjustable sensory settings, multiple modes of expression, and non‑immersive options for sensitive students).

Discussion

Taken together, the findings suggest a fundamental shift in how schools can think about behavioural crises. Instead of seeing outbursts as problems to be suppressed, they become moments where emotion, cognition, and relationship can be understood and reshaped. Digital tools do not replace the human relationship; rather, when used well, they act like extra “nervous system mirrors” in the classroom, making invisible processes visible so that students and adults can co‑regulate more effectively.

Within a PBIS framework, real‑time data from wearables, classroom apps, and AI dashboards move behavioural support from reactive to proactive. Rather than waiting for physical aggression or loud disruption, changes in HRV, skin conductance, or movement patterns can flag early stress escalation. Teachers can then offer pre‑agreed regulation options: short movement breaks, breathing exercises paired with biofeedback visuals, time in a VR calm room, or guided use of a self‑regulation app. Behavioural expectations are still clear, but the focus shifts from “you broke the rule” to “something is happening in your system—let’s respond skilfully.”

SEL‑aligned digital tools add a richer emotional layer. Gamified apps, journaling platforms, and emotion‑tracking interfaces give students daily practice in naming feelings, observing body signals, and experimenting with coping strategies. Over time, this scaffolds emotional literacy and reduces the need for adults to be the only regulators in the room. For many neurodivergent students, visual or symbolic representations of emotion (avatars, colours, or simple sliders) are less threatening and more precise than purely verbal check‑ins.

From a UDL perspective, digital tools expand the ways students can engage with emotional and behavioural learning. Instead of relying exclusively on spoken language or written reflection, learners can express internal states through icons, movement, soundscapes, or avatar choices. This flexibility is especially important for students with sensory sensitivities, language delays, or high levels of anxiety. Well‑designed VR and AR environments can be adjusted in intensity, speed, and sensory load, respecting each learner’s threshold while still providing meaningful practice.

The review also highlights that the success of these systems depends heavily on teacher mediation. Data without empathy can quickly become a new form of control. When teachers receive continuous streams of behavioural and emotional information, they also need a framework for interpreting it compassionately: recognising that spikes in arousal are often linked to unmet needs, previous trauma, or sensory overload, not “defiance.” Digital coaching platforms and VR‑based rehearsal of de‑escalation scenarios seem particularly helpful in building this reflective stance.

Another crucial insight is the ethical dimension. Emotional and physiological data are extremely intimate. When gathered for educational purposes, they must be handled with transparency, consent, and a strong focus on student agency. The review emphasises that participatory design—bringing students, families, and teachers into the conversation about what is measured, how it is used, and who sees it—is essential to prevent systems from sliding into surveillance. Similarly, cultural responsiveness is non‑negotiable: tools designed around one cultural script for “appropriate emotion” can easily misread or pathologise students from other backgrounds.

For clinical and applied neurofeedback practice, this literature is an important signal. It suggests that school systems are increasingly open to physiological and brain‑based self‑regulation tools, provided they are embedded in broader relational frameworks like PBIS and SEL, and supported with robust ethics and teacher training. While most of the interventions reviewed involve peripheral biofeedback (HRV, skin conductance) rather than EEG neurofeedback, the mechanisms overlap: strengthening prefrontal control, increasing interoceptive awareness, and transforming behaviour from an external compliance issue into an internal skill set.

In short, digital interventions can meaningfully reduce behavioural crises and increase self‑regulation and inclusion, but only when three conditions are met: (1) they are anchored in coherent pedagogical models (PBIS, SEL, UDL), (2) teachers are supported as ethical, reflective mediators, and (3) the systems are designed with equity, accessibility, and human dignity at their core.

Brendan’s perspective

When I read work like Simos’ review, I can’t help but feel that biofeedback and neurofeedback are standing at a crossroads. For decades, we have mostly asked people to adapt themselves to the environment: sit still in the clinic, watch the feedback display, learn to change your physiology, then try to carry that new skill back into your messy, noisy life. It is a bit like teaching someone to swim in a perfectly calm pool and then dropping them into the open ocean.

The next logical step is to flip that relationship and make it reciprocal. Instead of only asking the nervous system to adapt to the environment, we ask the environment to respond to the nervous system as well. Biofeedback and neurofeedback give us a window into physiology; digital interventions make it possible for the world outside the clinic to listen and adapt in real time.

In practical terms, that means moving from unidirectional training to genuine closed-loop ecosystems. In the clinic, a child might train SMR at Cz (12–15 Hz) to improve behavioural inhibition, with simultaneous down‑training of high beta (22–30 Hz) at Fz or Cz to reduce hyperarousal and hair‑trigger reactivity. In the classic model, we hope this learning generalises: when the child is back in the classroom, they should be better able to pause, tolerate frustration, and choose a different response.

In a reciprocal model, we still train these EEG patterns, but we also surround the child with environments that are sensitive to their physiology. A wearable might track HRV and subtle changes in movement; when arousal rises and variability drops, the classroom tablet automatically slows the pace of tasks, dims the visual intensity, or offers a brief breathing game that syncs with the child’s current rhythm. A simple vibration cue can remind them of the same SMR‑calm state they practised in the clinic. The environment bends slightly toward the brain, not just the other way around.

This is not about wrapping children in bubble wrap or removing all challenge. It is about matching challenge to capacity in real time. Think of it as a dynamic thermostat for the nervous system: we still want seasons, variability, and growth, but we avoid wild temperature swings that fry the circuitry. In neurofeedback terms, we are extending the feedback loop beyond the EEG screen and into the child’s lived world.

qEEG can serve as a cartography tool in this process. If an assessment shows, for example, elevated frontal and central high beta with reduced SMR and excess slow activity in fronto‑central regions, we have two levers:

• internal: train SMR at Cz or C4, inhibit excessive theta and high beta, and shore up fronto‑central regulation;

• external: reduce unexpected auditory load, create predictable transition rituals, build in micro‑breaks before known triggers, and pair these with HRV or breathing practice.

The same logic applies to anxiety‑related patterns where there may be excess fast activity and reduced posterior alpha. Protocols might focus on alpha up‑training at POz, with down‑training of high beta in frontal regions; outside the clinic, lighting, soundscapes, and task demands can be tuned to support that calmer posterior state rather than constantly yanking the system into hypervigilance.

Simos’ emphasis on school‑based digital tools points to where this could go: VR calm rooms that automatically adjust intensity based on heart rate, SEL apps whose difficulty scales with a student’s physiological load, teacher dashboards that suggest co‑regulation options when early stress signals appear. From a neurofeedback perspective, this offers a beautiful continuity: we teach the brain to recognise and stabilise helpful patterns, and we arrange the environment so those patterns are easier to access, not constantly punished.

There is, of course, a risk. If we are not careful, adaptive environments can slide into covert control. A classroom that changes lighting, sound, and digital content based on students’ physiology without clear consent and explanation can feel more like surveillance than support. For the model to remain ethical and growth‑promoting, three principles matter:

• transparency: learners and families know what is being measured, why, and how it changes the environment;

• agency: the young person can opt in, pause, or override adaptive features, and they are actively taught about their own signals rather than kept in the dark;

• reciprocity: the goal is not simply to make the student “easier to manage,” but to co‑create contexts where their nervous system can learn, explore, and recover.

For clinicians, this shift invites a broader view of treatment planning. EEG neurofeedback in the office becomes one node in a larger self‑regulation network that includes HRV apps at home, sensory‑friendly routines, clear relational safety, and school‑based supports. Protocol individualisation is not only about which frequencies we train at which sites, but also about which environments we intentionally reshape. A teenager with ASD and explosive outbursts might train SMR and frontal inhibition, practise HRV breathing before school, and use a tailored soundscape that ramps down stimulation during transitions. A young person with trauma might combine alpha/theta protocols with carefully titrated VR exposure and a classroom plan that respects their startle thresholds.

And this does not need to be limited to children. Athletes, creatives, and adults in high‑stress workplaces can benefit from the same idea: EEG and biofeedback in structured sessions, plus environments—digital and physical—that adapt to their physiology enough to keep them in a productive learning zone. You could imagine open‑plan offices where noise‑cancelling, task timing, or ambient lighting nudges are linked to aggregate HRV trends (with strict anonymisation) to prevent collective burnout, or training platforms where cognitive load is titrated to frontal theta/alpha dynamics in real time.

Clinical research will inevitably lag behind these possibilities, and many current studies still treat biofeedback and neurofeedback as isolated interventions. But the underlying principle is already visible in Simos’ review: when environments become more responsive to nervous systems, behaviour changes more easily and with less collateral damage. The task for our field is to harness that principle consciously, ethically, and creatively, so that neurofeedback does not remain a 30‑minute appointment on a chair, but becomes part of a lived, reciprocal dialogue between brains and the worlds they inhabit.

Conclusion

This narrative review paints a hopeful but nuanced picture of how digital tools can transform the management of intense behavioural outbursts in schools. Biofeedback wearables, AI‑driven dashboards, VR/AR environments, and gamified SEL platforms are not magic solutions, but they do create new ways to see, anticipate, and respond to dysregulation. When grounded in PBIS, SEL, and UDL, they help shift the culture from punishment and exclusion toward proactive skill‑building, empathy, and psychological safety.

For students, especially those with EBD, ADHD, autism, or trauma histories, these systems can increase frustration tolerance, emotional literacy, and a sense of agency. For teachers, they can reduce burnout and support a more reflective, relational approach to behaviour. Yet the review is clear: technology amplifies whatever philosophy is already present. Without ethical safeguards, cultural responsiveness, and solid professional development, the same tools risk reinforcing surveillance, inequity, and control.

For the broader neurofeedback and biofeedback community, this work underlines a key message: physiological self‑regulation training belongs not only in clinics but in everyday learning environments. When used thoughtfully, digital co‑regulation can turn behavioural crises into teachable moments and help schools become places where nervous systems learn—not just minds.

References

Simos, C. (2025). Managing intense behavioral outbursts in schools: Innovative digital interventions for student self‑regulation and teacher support. Global Journal of Engineering and Technology Advances, 25(3), 1–18. https://doi.org/10.30574/gjeta.2025.25.3.0339