The technological arsenal behind this shift is formidable. Artificial intelligence, the Internet of Things, digital twins, extended reality, and blockchain are converging inside metaverse-style platforms to create persistent, immersive, and interoperable digital representations of factories. According to the editorial, manufacturing systems that undergo this transformation gain new possibilities for optimizing production, systems, and services through enhanced collaboration capabilities. The promised benefits are concrete: reduced waste, increased throughput, and more effective workforce training. Yet the same powerful tools bring equally concrete problems, including security risks, compatibility headaches, and the substantial costs of both initial transformation and ongoing utilization of digital technologies.

One of the most striking findings highlighted in the editorial concerns small and medium enterprises. Rahamaddula and colleagues, in a study cited within the research topic, observed that while large enterprises have already begun their digital transformation, SMEs are often left behind. This gap matters enormously, the authors argue, because SMEs form a large percentage of the manufacturing capability of many countries, and losing the benefits of digitalization and smart manufacturing can impose significant costs on national economies. The main obstacle identified was not a lack of ambition but uncertainty itself: the difficulty of judging the cost-benefit balance of digital transformation when the payoff is unclear and the investment is not.

To attack that uncertainty, the researchers proposed a readiness assessment tool built on factors recognized by experts as contributing to the readiness level of an SME. The tool was applied to practical use cases, giving companies a structured way to gauge whether their organization, infrastructure, and workforce can absorb metaverse-era technologies before committing scarce capital. The editorial notes that generalizing such readiness assessment and digital system mapping tools is one of the key future directions for the field, a step that could turn a bespoke research exercise into a standard diagnostic for the manufacturing mainstream.

Collaboration emerged as the second great challenge in the research collected under this topic. Magas and colleagues highlighted the difficulty of data transfer between different domains, a problem that becomes acute as factories stitch together machines, software layers, and partners that were never designed to talk to each other. Robust system mapping that uses ontologies, formal descriptions of concepts and their relationships, could solve this problem, but the study found that building such mappings proved a significant challenge, especially for inexperienced practitioners who lack the specialized vocabulary and modeling skills that ontology engineering demands.

The proposed remedy is characteristically metaverse-shaped: the technologies of the industrial metaverse and their interoperability can be used to establish platforms of ontology standards, creating collaborative tools that support ontology framework application, enhance system resilience, and ensure smooth cross-domain operations throughout the unique life cycle of every domain. In other words, the same immersive and networked infrastructure that lets engineers walk through a virtual factory can also serve as the shared semantic ground on which heterogeneous systems exchange meaning. The editorial frames this as a genuine opportunity, arguing that digital transformation provides a path toward understanding extended and complex systems that would otherwise remain opaque.

Two further contributions shift the focus from obstacles to opportunities. Mourtzis examined the possibilities that digital twins open up for human-machine interaction. When traditional manufacturing systems undergo digital transformation to become cyber-physical systems, maintaining a human-in-the-loop approach often proves a challenge, as automation layers tend to push operators out of the decision cycle. Industrial metaverse platforms offer a solution by immersing humans directly into cyber-physical system operations, facilitating greater system understanding and collaboration, streamlining communications, and promoting system adaptability, which in turn feeds sustainability and resilience.

Aheleroff and colleagues, meanwhile, argued that Industry 5.0 does not replace Industry 4.0 but cooperates with it, utilizing enhanced versions of existing technologies that can boost system sustainability and resilience when operated around the pillar of human-centricity. Their analysis suggests that the collaboration of humans with advanced technologies adds significant value by enabling the utilization of the scalability offered by Industry 4.0 while maintaining high flexibility in production and quality in products. This is the technical heart of the mass personalization story: as market demands push manufacturers from Mass Customization toward Mass Personalization, the complexity burden grows relentlessly, and only the combined leverage of digital technologies and human judgment can absorb it without sacrificing efficiency.

The editorial is candid that deeper engagement with digital transformation keeps surfacing hard problems. Significant challenges discovered in the research include security, privacy, financial stability, standardization, and regulation. These are not incidental friction points; they are structural conditions that determine whether a factory can trust an immersive digital replica with production-critical decisions, whether workers’ data gathered in virtual environments stays protected, and whether small firms can finance the transition without betting their survival on unproven returns. Standardization in particular recurs throughout the collected studies as the precondition for the cross-domain collaboration that the metaverse vision promises.

Taken together, the research topic showcases several opportunities for digital transformation and the industrial metaverse to promote Industry 5.0 goals such as resilience and sustainability, opportunities that rely heavily on the immense potential of advanced digital technologies for data transfer, optimization, and collaboration. But such powerful tools proved understandably challenging to use, mostly due to their cost, complexity, and the uncertainty of their impact. The future directions laid out in the editorial, generalizing readiness assessment and digital system mapping tools, pursuing standardization to facilitate collaboration, and exploring novel application possibilities for system performance optimization, sketch a roadmap for turning an ambitious vision into factory-floor practice. For manufacturers weighing the leap into the industrial metaverse, the message is measured: the technology is ready to deliver human-centered, sustainable, and resilient production, but only for organizations that plan for the costs, the standards, and the people as carefully as they plan for the software.

Subject of Research: Digital transformation of manufacturing through the industrial metaverse for Industry 5.0

Article Title: Editorial: Editor’s challenge in digital manufacturing – digital transformation of manufacturing through industrial metaverse: opportunities and challenges for industry 5.0

Article References: Mourtzis, D. (2025). Editorial: Editor’s challenge in digital manufacturing – digital transformation of manufacturing through industrial metaverse: opportunities and challenges for industry 5.0. Frontiers in Manufacturing Technology, 5, Article 1754316. https://doi.org/10.3389/fmtec.2025.1754316

Image Credits: AI Generated

DOI: 10.3389/fmtec.2025.1754316

Keywords: Industry 5.0, industrial metaverse, digital manufacturing, digital twin, cyber-physical systems, human-machine interaction, SMEs, digital transformation, sustainability, resilience, mass personalization, interoperability

Human Digital Twins have emerged as a powerful paradigm in industry and healthcare, typically built on musculoskeletal models, physiological monitoring, and biomechanical simulation. These systems can predict body movement, muscle fatigue, workload, or injury risk with impressive fidelity. But the authors argue that such models are fundamentally incomplete for modern environments. Contemporary humans operate in complex multisensory settings characterized by multiple competing sensory streams, information overload, sensory conflict, and sensory strain. Think of a factory floor filled with machinery noise, visual clutter, and constant interruptions, or a virtual reality interface that generates conflicting visual and vestibular signals. These conditions place heavy demands on perceptual and attentional resources, and their effects on behavior cannot be captured by modeling physical characteristics alone.

Current neuroergonomic systems, the researchers note, remain largely descriptive rather than predictive. Many existing approaches analyze physiological signals or behavioral markers after the fact to infer cognitive states such as workload or stress. While valuable, these methods are limited in their ability to anticipate future cognitive states or support proactive adaptation. They treat cognition as a snapshot rather than a continuous, dynamic temporal process, and they often fail to generalize across tasks, individuals, and real-world conditions. Machine learning architectures such as Long Short-Term Memory networks and transformers can model temporal dependencies and enable forecasting, while reinforcement learning supports sequential decision-making, but the authors point out that these approaches are fundamentally passive and data-driven, relying on reward engineering and brute-force trial-and-error exploration. Crucially, none of them account for biological attention as it is understood in computational neuroscience, where attention operates as precision weighting, a mechanism that dynamically adjusts how much influence each sensory channel has on the brain’s internal model of the world.

The S-HDT framework is grounded in active inference, a theory from computational neuroscience that treats perception, cognition, and action as parts of a single unified process of probabilistic inference. Rather than passively receiving sensory data, an active inference system maintains a generative model of the world, continuously predicting what sensory input it should expect and updating its beliefs when predictions fail. The S-HDT applies this principle to model a human operator’s latent cognitive states, including cognitive load, attention, fatigue, emotion, and task engagement. These hidden states are inferred from multimodal observations, which may include physiological measures such as heart rate variability and muscular activity, behavioral cues, and environmental context. The model continuously minimizes the discrepancy between predicted and observed sensory data, producing progressively more accurate representations of the operator’s current cognitive state as an evolving probability distribution rather than a fixed label.

At the mathematical heart of the framework lies variational free energy, a quantity that serves as an upper bound on surprise, the unexpectedness of incoming observations. The free energy expression combines two terms: an expectation term that measures how well the surrogate twin’s current beliefs explain the observed data, essentially its prediction error, and an entropy term that reflects uncertainty about those beliefs. High entropy means several explanations remain possible, while low entropy indicates confidence. By minimizing free energy, the S-HDT approximates Bayesian inference over hidden cognitive states without needing to compute intractable posterior distributions exactly. Importantly, the authors emphasize that free-energy minimization should not be understood as minimizing environmental complexity itself. Surprise is relative to the model’s predictions and the operator’s current goals. In a crowded cocktail party scenario, the operator actively samples task-relevant information through attentional allocation, gaze, and head orientation, and the S-HDT maintains a suitable level of uncertainty for adaptive inference by continuously updating its internal belief distribution.

The framework also introduces an organizational distinction between core and mantle cognitive processes. Core processes correspond to evolutionarily conserved mechanisms that operate across most vertebrate species, including predictive modeling of sensory information, attentional allocation, action control, and threat detection. These provide the fundamental computational infrastructure that enables organisms to survive in uncertain environments by rapidly predicting and responding to external events. Mantle processes, by contrast, represent higher-order cognitive functions strongly shaped by learning, culture, and experience, such as goal management, social cognition, language processing, and symbolic reasoning. The authors stress that this distinction is a functional modeling abstraction rather than a strict anatomical or evolutionary dichotomy, intended to organize processes with different temporal and contextual characteristics while retaining their continuous, bidirectional interactions within the unified generative framework.

One of the most ambitious proposed capabilities is predictive cognitive simulation, in which a sufficiently validated implementation could generate plausible future trajectories of human cognitive states rather than merely estimating current ones. This matters because neuroergonomic environments benefit enormously from predicting performance degradation, workload surges, or attentional lapses before they become apparent in observable behavior. The framework performs continuous cognitive state estimation, constructs environmental mappings that relate task contexts and conditions to cognitive states, supports adaptive interfaces and task allocation strategies personalized to individual users, and functions as an intelligent decision-support system recommending interventions that minimize cognitive overload. The generative nature of the model also enables counterfactual reasoning, allowing the system to answer what-if scenarios by changing prior beliefs or sensory inputs within the generative model and propagating their effects forward, evaluating how changes in task difficulty or environmental stressors would affect physiological metrics without exposing human operators to unnecessary risk.

To illustrate the framework’s behavior, the team presented a proof-of-concept simulation comparing two neuroergonomic scenarios: a quiet environment with low external distraction and a cocktail party environment characterized by multiple competing sensory stimuli. The simulation deliberately instantiated only a restricted subset of the broader framework, focusing on auditory attention and perception-action dynamics, and did not model speech intelligibility, linguistic content, or informational masking. The auditory scenes were represented by 64-dimensional binaural spectral features, with spatial information encoded through left-right gain modulation and frequency-dependent spectral shaping. Sensory inputs passed through a hierarchical scale-space predictive processing model that calculated multi-level prediction errors, and the simulated agent interacted with the environment in a closed loop, updating its heading and attentional focus based on prediction error dynamics. Abrupt increases in free energy induced exogenous attentional shifts, simulating reflexive redistribution of cognitive resources under high load. Across all measured quantities, including free energy dynamics, attentional behavior, tracking performance, and cognitive effort, values were consistently larger in the cocktail party scenario than in the quiet environment, suggesting that rich stimulus environments involve greater uncertainty and prediction errors, requiring continual belief updating. The authors are careful to note that this simulation illustrates the behavior of selected framework components rather than providing empirical validation of human cognitive-state prediction.

The researchers are equally candid about the framework’s structural limitations. A fundamental challenge is the identifiability of latent cognitive states, since multiple internal states may produce similar observable physiological or behavioral patterns. Within active inference, the relationship between biosignals such as cardiac, electrodermal, or muscular activity and the latent affective, motivational, and attentional states they are meant to reveal is typically framed through a Markov blanket, a statistical boundary that renders internal states conditionally independent of the environment. But the authors caution that this boundary is defined relative to a chosen model, level of coarse-graining, and timescale, making it observer-dependent. The mapping from peripheral signals to internal states is many-to-one and not invertible, partly because autonomic signals are non-specific and, for affective states in particular, there are no fixed physiological signatures. Interoceptive inference accounts treat emotion not as a hidden variable passively lying behind the blanket but as a construction built from interoceptive signals together with contextual and conceptual priors, so the same peripheral pattern can support different affective interpretations. Physiological variables are also actively regulated through allostasis, acting simultaneously as evidence about and control over internal states, which makes the boundary partly circular. Active inference, the authors conclude, constrains the state identifiability problem rather than dissolving it, and the S-HDT therefore treats latent cognitive states as probabilistic surrogate representations whose uncertainty should be retained explicitly.

Practical challenges also remain in real-time multimodal data fusion, generalization across users with limited calibration data, and ethical deployment. Continuous inference of cognitive states raises issues of transparency, user autonomy, and data privacy, and the authors argue that predictive neuroergonomic systems must be designed with explainability and human-in-the-loop control, including communicating prediction uncertainty to users and requiring human confirmation before consequential adaptive interventions. Despite these hurdles, the S-HDT represents a conceptual step toward a new paradigm in neuroergonomics, one in which cognitive modeling is dynamic, predictive, and personalized rather than static and retrospective. By unifying perception, cognition, and action under a single computational principle and embedding prediction directly into the inference loop, the framework opens new opportunities for investigating neuroergonomic system design, human-centered artificial intelligence, and adaptive human-machine interaction, with future work focused on multimodal human-data validation and application-specific operationalization.

Subject of Research: A computational framework using active inference to model embodied human perception and cognition in digital twins for neuroergonomics

Article Title: Using a surrogate human digital twin to model embodied perception and cognition for neuroergonomics

Article References: Chowdhury, T. A., Francis, A. L., & Strauss, D. J. (2026). Using a surrogate human digital twin to model embodied perception and cognition for neuroergonomics. Frontiers in Neuroergonomics, 7, Article 1939412. https://doi.org/10.3389/fnrgo.2026.1939412

Image Credits: AI Generated

DOI: 10.3389/fnrgo.2026.1939412

Keywords: human digital twin, neuroergonomics, active inference, free energy principle, cognitive modeling, embodied cognition, multimodal sensing, attention, predictive simulation, human-machine interaction, surrogate model, human-centered AI

The study arrives at a moment of tension in the deployment of self-driving technology. On the technical side, the numbers are impressive. Drawing on prior real-world evidence, the authors note that across 56.7 million rider-only miles, autonomous systems showed crash-rate reductions of roughly 79 percent for injury-reported crashes, 81 percent for airbag-deployment crashes, and 85 percent for suspected serious-injury crashes compared with human benchmarks. Bodily-injury insurance claims in fully driverless operations fell by as much as 92 to 93 percent, and police-reported injury crashes dropped by about 55 percent in some analyses. Since human error contributes to roughly 94 to 95 percent of road collisions, these gains represent a genuine transformation in road safety.

Yet the study argues that functional safety and perceived safety are fundamentally distinct constructs, and that conflating them is a mistake with real consequences for public acceptance. The researchers, led by Anu Masso of Tallinn University of Technology together with colleagues in Hong Kong and Denmark, designed a semi-experimental online study in which participants viewed paired traffic scenarios presented under both autonomous and non-autonomous conditions. The scenarios deliberately featured a wide range of social diversity cues, including gender, age, race operationalized through skin-tone variation, income, disability, and micromobility, grounded in theories of super-diversity and intersectionality. Areas of interest were matched in size and position across paired images to rule out low-level visual saliency as a confound.

Eye tracking was conducted through the RealEye platform, which uses webcam-based AI gaze prediction with an accuracy of roughly 110 pixels. Each trial began with a 40-point calibration and a 9-point validation check, and participants whose calibration errors exceeded 2.5 degrees of visual angle or who failed attention checks were excluded. The primary measure was fixation total time, the cumulative dwell time spent on each social diversity area of interest, chosen for its robustness in webcam-based settings and its interpretability as sustained attentional engagement. Revisit counts served as secondary validation. The team then used Bayesian multilevel regression models, estimated with four Markov Chain Monte Carlo chains in the brms package, to link attention patterns with self-reported safety and security evaluations while accounting for individual and city-level variation.

The first major finding concerns the structure of visual attention itself. Gaze patterns toward social groups proved to be highly organized and remarkably stable across both autonomous and non-autonomous scenarios. Categories such as age, race, and income consistently attracted higher levels of attention in both contexts, while disability-related and COVID-related cues remained comparatively backgrounded. Automation modestly increased overall attention to social cues without fundamentally reshaping these hierarchies. The estimated association between attention indices in the two contexts was moderate, with a beta of approximately 0.66 and a 95 percent confidence interval spanning 0.44 to 0.88, indicating that attentional structure is partially stable but also partially reconfigured by mobility context. In other words, autonomous mobility amplifies existing perceptual salience hierarchies rather than creating new ones.

The surprise came when the researchers connected those gaze patterns to feelings of safety. At the individual level, correlations between visual attention indices and perceived safety or security were weak, with absolute correlation values below 0.2, and scatter plots revealed no clear linear associations. In the final Bayesian models, the key predictor was the difference in attention between autonomous and non-autonomous contexts, and its posterior estimates were centered near zero with credible intervals overlapping zero for both outcomes. Once trust, social positioning, and regional context were accounted for, shifts in visual attention simply did not translate into changes in experienced safety or normative security. Being perceptually alert to social difference, it turns out, is not the same as feeling protected by it.

What did predict perceived safety? Trust emerged as the dominant force. Both general trust in automation, measured across SAE automation Levels 0 through 5, and situational trust in autonomous transport applications showed consistently positive posterior estimates for experienced safety. Notably, trust remained relatively stable across automation levels 0 to 4, with mean scores between 3.40 and 3.49, but dropped to 3.10 for Level 5 full automation, signaling persistent hesitation toward fully driverless systems even among people otherwise comfortable with semi-automated technology. Trust also varied by who was being transported: respondents expressed the highest trust in autonomous vehicles carrying people from rural to urban areas and the lowest in vehicles carrying elderly people to parks, suggesting that acceptance depends on the perceived vulnerability and social status of passengers.

Security values followed a different explanatory logic altogether. Unlike perceived safety, security as a normative principle was less strongly associated with trust and more strongly shaped by social position and cultural context. Minority status, measured through self-identification as belonging to a national minority group, was the most robust individual-level predictor, with minority respondents systematically assigning lower importance to security values and reporting lower perceived safety even after controlling for trust and attention. Regional effects were substantial in both models, with patterns more pronounced for security values than for perceived safety, reinforcing the interpretation that security evaluations are deeply anchored in cultural and institutional environments. Higher perceived safety was generally reported by respondents with higher education and by those living in European and East and Southeast Asian regions, while younger respondents aged 18 to 29 and those in the Americas or Sub-Saharan Africa reported lower levels.

The broader survey results add texture to this picture. Nearly 30 percent of respondents felt that transport safety in their city had remained unchanged over the previous five years, while roughly 55 percent perceived an increase and about 16 percent a decrease, indicating that even perceptions of objective safety trends are filtered through local experience. Meanwhile, security as a public value was overwhelmingly salient: 54.7 percent rated it as very important in the development of AI-based urban technologies, and only 13 percent considered it insignificant. These patterns suggest that while people care intensely about security in the abstract, their concrete feelings of safety in automated mobility are constructed through trust, social identity, and institutional context rather than through what their eyes happen to land on.

The implications for the autonomous vehicle industry and for AI ethics are significant. A widespread assumption holds that making diverse social groups visible and recognizable within mobility environments is sufficient to foster inclusion, trust, and safety. This study challenges that assumption directly. Visual representation may be necessary for functional safety, ensuring that systems reliably detect vulnerable road users, but it does not guarantee psychological reassurance or normative legitimacy. The authors call for complementing safety-by-design with socio-psychological safety-by-design, in which trust calibration, cultural legibility, and social recognition extend beyond what systems detect or display. The study also acknowledges limitations, including the lower spatial precision of webcam-based eye tracking compared with laboratory systems, the possibility that visual stimuli unintentionally encoded stereotypes, and small sample sizes in some cities, which the authors address through partial pooling in their Bayesian models. Future research, they suggest, should combine laboratory-grade eye tracking, in situ mobility experiences, and qualitative approaches to uncover the mediating mechanisms, such as institutional trust and prior experience with automation, that connect perception to judgment. For now, the message is clear: legitimate autonomous mobility will not be won by crash statistics or visible diversity alone, but by the social and institutional conditions under which people come to feel genuinely safe.

Subject of Research: Cross-cultural eye-tracking research on perceived safety and trust in autonomous vehicle environments

Article Title: What shapes perceived safety in autonomous mobility? Cross-cultural evidence from large-scale online eye tracking

Article References: Masso, A., Pietarinen, A.-V., Ibrahimi, M., & Ben Yahia, S. (2026). What shapes perceived safety in autonomous mobility? Cross-cultural evidence from large-scale online eye tracking. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03278-1

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03278-1

Keywords: autonomous vehicles, eye tracking, perceived safety, trust in automation, AI ethics, cross-cultural study, social diversity, Bayesian multilevel models, data justice, minority status, human-machine interaction, urban mobility

The new system, described in the journal Human Factors, adds two haptic zones to a steering wheel at the ten o’clock and two o’clock positions. These zones can expand or contract, inflating under the driver’s palms to express the intention of the automated driving system. Crucially, the communication is not just one-directional. Drivers can respond by squeezing the haptic zones, signaling agreement or disagreement with the computer’s proposed maneuver before it happens. According to Hannah Báez, a robotics Ph.D. student and first author of the study, this ability to negotiate before an action is taken allows driver and machine to resolve conflicts in advance rather than clashing over control mid-maneuver.

To test the concept, the research team placed drivers in a driving simulation centered on an upcoming turn. The scenario was deliberately designed to create friction. Sometimes the driver and the automation would agree on which direction to turn; sometimes they would disagree. To raise the stakes further, the simulation could introduce sudden obstacles into the driver’s planned path in either situation. As the driver approached the turn, the semi-autonomous system inflated the left or right haptic portion of the steering wheel to indicate the direction it intended to go. A driver who disagreed could squeeze that portion of the wheel to register the objection and negotiate a new plan. If an obstacle appeared in the driver’s chosen path, the wheel pulsed on the corresponding side to deliver a tactile alert.

The results were striking when compared against two baselines: conditions in which drivers received no information at all, and conditions in which the automation displayed its intentions one-way, without any channel for the driver to respond. With the two-way haptic negotiation interface, driving performance improved measurably. Brent Gillespie, professor of robotics at the University of Michigan and senior author of the study, reported that drivers displayed smoother and more accurate driving paths, fought less with the automation, and used less braking while maneuvering with greater confidence. In other words, when the car and the driver could hash out a shared plan through touch, the resulting driving was simply better.

The benefits extended beyond the trajectory of the vehicle itself. Participants in the negotiation condition reported significantly lower workload, including reduced effort, frustration, and physical demand. This finding matters because mental workload is a well-documented problem in modern driver-assistance systems. When a car communicates through a barrage of auditory beeps and flashing visual alerts while simultaneously tugging at the wheel, the driver is forced to parse multiple competing channels of information under time pressure. A haptic channel embedded directly in the point of contact between driver and vehicle consolidates that communication into a single, intuitive medium, one that requires no glance away from the road and no interpretation of abstract warning symbols.

Perhaps the most consequential result concerned trust, the fragile currency on which the entire enterprise of semi-autonomous driving depends. After the automated system made a mistake, such as missing an obstacle or issuing a false warning, drivers using the negotiation interface recovered their trust in the system much faster than those relying on traditional one-way communication. The researchers attribute this resilience to the structure of the interaction itself. When driver and automation communicate intent, exchange feedback, and adjust to one another, they begin to operate as a team with transparency in decision-making, rather than as two agents taking turns behind the wheel and second-guessing each other in between.

The team is careful to note that the technology is not without risks. Some drivers became overly trusting after experiencing successful negotiations with the system, a phenomenon that raises familiar concerns about automation complacency. If a steering wheel negotiates well a dozen times in a row, a driver may come to assume it will always negotiate well, potentially disengaging their own vigilance at precisely the moments it matters most. The researchers emphasize that further study of trust dynamics is needed before such a system reaches the road, particularly around how drivers calibrate their reliance on the automation over longer exposure and across more varied driving conditions.

The technical elegance of the approach lies in its use of shared haptic channels for what human-factors researchers call shared planning and control. Conventional driver-assist systems tend to intervene silently, correcting steering without warning and leaving the driver to discover the machine’s decision through the physical sensation of resistance. That sensation of fighting for control, combined with overwhelming visual and auditory warnings, is precisely what drives some users to disable semi-autonomous features altogether. By contrast, the haptic negotiation interface makes the automation’s intentions legible before they become actions, and gives the driver a low-effort, high-bandwidth means of dissent. The wheel becomes a medium of dialogue rather than a battleground.

The implications reach well beyond the laboratory simulation. As automakers race to deploy increasingly capable driver-assistance features, the question of how humans and machines share authority has become one of the central challenges of the field. Regulatory frameworks and safety standards increasingly recognize that the interface between driver and automation is as important as the underlying algorithms. A steering wheel that can express intent, receive feedback, and support genuine negotiation offers a template for that interface, one grounded in the oldest and most trusted sensory channel a driver has: the sense of touch. The technology is covered by U.S. patent US12227190B2, applied for with the assistance of U-M Innovation Partnerships, and the team is actively seeking partners to bring it to market.

The study, titled Haptic shared planning and control: enabling coordination of future actions in human-autonomous vehicle teams through a haptic negotiation interface, was authored by Hannah Báez along with Haochi Pan, Nadine Sarter, and Brent Gillespie of the University of Michigan, and Jean Costa and John Gideon of the Toyota Research Institute. While one-way information remains the current norm for communication between autonomous vehicles and their human occupants, this research demonstrates the tangible benefits of a fuller two-way dialogue, one in which driver and automation exchange intent, agreement, and action. If the steering wheel of the future can negotiate, the drivers of the future may finally stop fighting their cars and start working with them, arriving safer, calmer, and considerably more willing to let the automation help.

Subject of Research: Haptic negotiation interfaces for communication between drivers and semi-autonomous vehicle systems

Article Title: Steering wheel communicates for better semi-autonomous driving performance

Article References: Steering wheel communicates for better semi-autonomous driving performance. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: haptic feedback, semi-autonomous driving, human-machine interaction, steering wheel interface, driver trust, automation, University of Michigan, Toyota Research Institute, Human Factors, driver-assistance systems, shared control, automotive robotics

The study, published in the journal AI & Society by Yasmin A. Carruthers and Johannes M. Heim, compared podcasts generated by NotebookLM with naturally produced human podcasts on the same topics, examining two hallmarks of human conversation: the timing of turn-taking and the way speakers adopt contextually appropriate conversational roles. The researchers argue that naturalness in dialogue requires far more than getting the timings right; it demands a contextually appropriate, addressee-oriented approach that responds to how a conversation actually unfolds.

Human turn-taking is a marvel of predictive coordination. Decades of research, beginning with the landmark work of Sacks, Schegloff and Jefferson in 1974, have established that conversation is governed by an organized system in which only one person typically speaks at a time, transitions are smooth, and gaps or overlaps are minimized. Listeners exploit morphosyntactic, semantic and prosodic cues to anticipate when a speaker’s turn will end, preparing their responses while the previous speaker is still talking. Average turn transitions fall in the range of roughly 200 to 250 milliseconds, far shorter than the approximately 600 milliseconds humans need to plan and produce a spoken response. This means that human interlocutors must be predicting, not merely reacting, and traces of this ability appear remarkably early in development, with infants showing sensitivity to conversational turn structure from birth.

Yet the “no-gap, no-overlap” principle is not absolute. Subsequent research has shown that overlaps can account for a substantial share of speaker transitions, and that brief overlaps often signal engagement rather than disruption. Backchannels, the brief vocal acknowledgments such as “mm-hm” or “right” that listeners produce without claiming the floor, add a dynamic and expressive quality to human dialogue. In podcast settings, hosts and guests also follow genre-specific social scripts: hosts introduce topics and then step back into a novice role to give guests a platform, while guests predominantly adopt expert stances. These role expectations, rooted in the broadcasting heritage of podcasting, make conversational behavior highly predictable despite frequent shifts between expert and novice positions.

Against this backdrop, the researchers annotated approximately ten-minute excerpts from two human podcasts and two NotebookLM-generated podcasts built from their transcripts, using the acoustic analysis software Praat. They coded speaker contributions, role shifts, turn durations, overlaps and transitions, achieving very high inter-annotator agreement. The human podcasts came from a series on productivity, featuring a recurring host and invited guests, while the AI versions were generated by NotebookLM using Gemini-based script writing and Google’s own text-to-speech voices, with no additional training data supplied.

On the surface, the AI podcasts behaved similarly to their human counterparts: questions structured the conversation, topics were expanded through follow-ups, and both hosts and guests provided feedback through backchannels. A statistical model confirmed that guests, whether human or artificial, produced significantly more expert contributions than hosts, in line with the conventions of the podcast genre. But the deeper measurements told a different story. Human turns were dramatically longer, averaging 22.3 seconds for hosts and 49.4 seconds for guests, compared with just 11.3 and 14.8 seconds for the AI agents. Human distributions of turn length were skewed toward long, developed contributions, while AI turns were short and symmetrically distributed.

The most striking divergence appeared in overlapping speech. Human guests overlapped for an average of more than four seconds and human hosts for over two seconds, whereas AI overlaps lasted mere milliseconds. AI backchannels rarely overlapped at all; instead, the current speaker paused, waited for the backchannel to be uttered, and then resumed, producing a mechanical politeness that no human interlocutor would display. The researchers interpret human overlaps as signals of engagement and emotional alignment, made possible by the fact that human listeners continuously predict where turns will end. NotebookLM, by contrast, sidesteps the problem of endpoint prediction entirely by pre-planning the entire dialogue script before converting it to speech, strictly enforcing the avoidance of gaps and overlaps in a way that humans never do.

The study also tested a newer NotebookLM feature that allows a human to join the AI conversation live, and the results were revealing. Every human intervention disrupted the pre-planned script: transitions slowed dramatically, overlaps vanished, audio glitches caused sudden voice switches, and the agents could only acknowledge the human’s contribution after long, silence-based delays before returning to their prepared narrative. Because spontaneous intervention requires resource-intensive, real-time context integration that pre-planning was designed to avoid, the hybrid dialogues lost the fluidity that made the fully scripted versions sound convincing. The authors conclude that pre-determining role behavior and turn-taking only goes so far, and that NotebookLM clearly hits its limits when responding to unpredictable input.

Taken together, the findings suggest that AI-generated dialogue can mimic individual features of natural conversation in isolation but fails to synthesize them into the dynamic negotiation of information and social relation that defines human talk. The AI agents spoke shorter, overlapped far less, and behaved more like two equally informed participants than like a host giving a guest a platform, breaking with genre conventions that listeners intuitively expect. Human conversation, the authors argue, is not optimized for seamless information exchange but for building common ground, an emergent and collaborative process shaped by mutual attention, verbal feedback and shared, growing context. Variable turn-taking timings and adaptive conversational role behavior remain unmatched human skills, and achieving genuine naturalness will require AI systems that can interpret social contexts and respond dynamically to the fluid negotiation of interlocutor relations, not merely imitate their surface elements.

Subject of Research: Turn-taking timings and conversational role behavior in AI-generated podcast dialogues compared with natural human conversation

Article Title: Not your average podcaster: turn-taking timings and conversational role behavior in AI-generated dialogues

Article References: Carruthers, Y. A., & Heim, J. M. (2026). Not your average podcaster: turn-taking timings and conversational role behavior in AI-generated dialogues. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03365-3

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03365-3

Keywords: AI-generated podcasts, NotebookLM, turn-taking, conversational roles, dialogue generation, backchannels, overlaps, knowledge asymmetry, common ground, pragmatics, human-machine interaction, speech technology

Single-modal sensors, which detect one quantity at a time, are now mature enough to be printed, woven and laminated onto skin-like substrates. But the human body and its environment rarely deliver stimuli one at a time. A wearable patch pressed against sweating skin experiences mechanical deformation, a rise in humidity and a shift in temperature simultaneously, and its output is a convolution of all three. Multimodal intelligent sensors are designed to capture this multidimensional information in a single device, yet the review emphasizes that their performance remains constrained by overlapping material response windows, signal crosstalk caused by structural coupling, and the sheer difficulty of decoding the resulting complex datasets. Two channels on the same chip can end up answering each other’s questions.

To organize the field, the authors adopt a structured narrative-review framework built on four pillars: advanced material design, perception-decoupling strategies, artificial intelligence-driven data analysis and system deployment. The first pillar surveys the major transduction pathways used to convert physical and chemical stimuli into electrical signals, covering pressure, strain, temperature, humidity, gas and biochemical sensing. Each pathway carries its own trade-offs between sensitivity, response time, dynamic range and mechanical compliance, and the choice of transduction mechanism largely determines how well a device can later separate one stimulus from another.

The materials themselves form the second pillar, and the review catalogs a strikingly diverse toolbox. Hydrogels, with their tissue-like softness, ionic conductivity and controllable swelling, offer a natural route to humidity and strain sensitivity while remaining comfortable against skin. Carbon-based composites and two-dimensional materials bring exceptional electrical conductivity, large surface areas and tunable band structures, enabling highly sensitive resistive and capacitive readouts at low power. Janus heterogeneous structures, which combine two chemically distinct faces in a single particle or film, exploit asymmetric responses so that one side reacts to one stimulus class while the other responds preferentially to a different one. Biomimetic textiles weave these functions into fabrics, embedding sensing into clothing that people actually want to wear. Across all of these platforms, the unifying design goals are flexibility, conductivity, interfacial regulation and multifunctional integration, ensuring that adding modalities does not destroy the mechanical comfort or durability that makes wearables viable in the first place.

The technical heart of the review is its deep treatment of decoupling strategies, the engineering answers to crosstalk. Structural spatial decoupling physically separates sensing elements so that each stimulus interacts primarily with its designated channel, an approach that is intuitive but costly in device area and packaging complexity. Orthogonal responses of functional materials take a more elegant route: materials are selected or engineered so that their response matrices to different stimuli are as linearly independent as possible, meaning temperature changes one output in a pattern that pressure cannot mimic. Microstructure and interface engineering tunes porosity, surface chemistry and layered architectures to sharpen selectivity at the material level. The construction of independent signal channels gives each modality its own electrical pathway, reducing parasitic coupling, while feature-representation-based assisted unmixing pushes part of the separation task into the software, using learned feature spaces to statistically disentangle mixed signals that no passive design can fully separate.

Read together, these strategies describe a layered defense against ambiguity. The review makes clear that no single technique achieves low-crosstalk, high-fidelity perception under genuinely concurrent multi-stimulus conditions; instead, state-of-the-art devices combine spatial layout, orthogonal material chemistry and algorithmic unmixing, assigning each layer the portion of the separation problem it handles best. This cross-layer view is one of the paper’s most useful contributions, offering practical design guidelines for material selection, structural engineering and algorithm configuration rather than treating each as an isolated discipline.

Artificial intelligence completes the loop. The review examines how data-driven models now underpin multimodal signal recognition, fusion-based decision-making and scenario understanding. Machine learning models trained on labeled multimodal datasets can learn the characteristic signatures of, for example, a pulse waveform riding on a temperature drift, and can classify complex gestures or physiological states that no single channel could distinguish. Fusion-based approaches combine evidence across modalities to make decisions that are more robust than any individual sensor’s verdict, while scenario-understanding models move the system from raw perception toward contextual interpretation, such as recognizing that a subject is exercising rather than feverish. This is where the title’s promise of intelligence becomes literal: the sensing hardware provides rich but ambiguous data, and the learning algorithms provide the decoding machinery.

The applications surveyed span wearable health monitoring, electronic skins and human–machine interaction. In health monitoring, multimodal patches can track pulse, respiration, skin temperature and humidity together, offering clinicians a multidimensional physiological picture rather than isolated vital signs. Electronic skins for prosthetics and robots must distinguish a hot object from a heavy one at a glance, a task that demands exactly the decoupling and fusion capabilities the review describes. In human–machine interfaces, gesture recognition and tactile feedback both depend on reliably separating intentional mechanical signals from environmental noise. The authors frame the field’s trajectory as an evolution from device-level sensing toward system-level cognition, in which the sensor, the material, the algorithm and the application form a single designed pipeline.

The review does not shy away from the field’s bottlenecks. It identifies material stability, decoupling capability, sensing accuracy, long-term reliability, system integration and scalable manufacturing as the major obstacles between current laboratory prototypes and deployed products. Hydrogels dry out; two-dimensional films delaminate; machine learning models degrade when training data fail to match real-world distributions; and fabrication processes that work at wafer scale rarely translate to roll-to-roll textile production. Looking forward, the authors call for next-generation multimodal sensing platforms featuring high selectivity, self-powering capability, manufacturability, interpretability and edge-intelligence-enabled collaboration, so that sensing and computation can be distributed across networks of low-power devices rather than concentrated in the cloud.

What emerges from this synthesis is a coherent blueprint for a technology that could quietly become as ubiquitous as the smartphone camera. A flexible sticker that simultaneously reads touch, temperature, sweat chemistry and ambient gas composition, decodes the mixture on a nearby edge processor and reports a meaningful health state is no longer a speculative concept but an engineering program with named materials, named algorithms and named hurdles. By binding materials science, device physics and machine learning into one analytical framework, the review offers researchers a shared vocabulary for the interdisciplinary work ahead, and offers the rest of us a preview of electronics that will not just touch the world but genuinely understand it.

Subject of Research: Development of AI-empowered multimodal intelligent sensors using advanced functional materials and signal decoupling strategies for wearable and robotic applications

Article Title: AI-empowered multimodal intelligent sensors integrated with decoupling and advanced functional materials

Article References: Li, Y., Tian, Y., Luo, J., Wang, J., Cheng, J., Suo, C., Wang, C., Li, Z., Haiping Li, & Chen, X. (2026). AI-empowered multimodal intelligent sensors integrated with decoupling and advanced functional materials. Advanced Composites and Hybrid Materials, 9(5), Article 429. https://doi.org/10.1007/s42114-026-02067-0

Image Credits: AI Generated

DOI: 10.1007/s42114-026-02067-0

Keywords: multimodal sensors, flexible electronics, wearable technology, electronic skin, hydrogels, signal decoupling, machine learning, human-machine interaction, health monitoring, two-dimensional materials, biomimetic textiles, edge intelligence