Enhancing Higher-Education Governance Through Telepresence Robots and Gamification: Strategies for Sustainable Practices in the AI-Driven Digital Era

Abstract

Integrating telepresence robots with gamification opens up new directions in which higher-education governance could translate into higher levels of student engagement in the AI-driven digital era. Drawing on both constructivism and the self-determination theory (SDT), this study will review evidence on how these technologies enhance autonomy and motivation, boosting effective participation in diverse learning environments. The experiments were carried out across various subjects with socio-economically varied groups of students by deploying gamified learning modules on telepresence robots. Primary metrics under consideration involved participation rates, task completion times, and other qualitative feedback measures about impacts created by such technologies. Indeed, the participation rates of the robot group were 40% higher, task completion times were reduced by 30%, and module completion rates for the robot group were 30% improved. The students reported positive emotions and showed more engagement, with the low-income students showing an 80% engagement rate compared to 40% in the control group. The results from both robots and gamification show promising potential for reshaping traditional learning paradigms, especially for students from geographically distant and underserved areas. The study guides further research on applying advanced tools in higher-education governance to foster sustainable practices in the AI era.

1. Introduction

The integration of telepresence robots in education offers promising opportunities to revolutionize instructional technology by enhancing engagement and inclusivity. These robots provide real-time audio and visual communication, enabling a sense of physical presence for remote users. Initially developed for professional and medical settings, telepresence robots have demonstrated their potential to transform learning experiences. Early trials indicated that these robots enable remote students to actively participate in on-campus classes, narrowing the physical gap between them and their peers [1]. They also facilitate interactive activities such as virtual field trips and remote site visits, broadening learning opportunities for students unable to attend physically [2].

The COVID-19 pandemic accelerated this trend even further, as it ensured academic continuity during lockdowns and mandates for distance learning [3]. While these uses underscore their utility, the current use cases mostly revolve around remote attendance. The prospect of using them as a means for immersive and interactive learning experiences is extremely underexplored. Gamifying learning modules with telepresence robots can transform education by motivating students to become more participatory. It is well documented that gamified content, designed with self-paced challenges and interactive quests, indeed improves learning outcomes [4,5]. Neo and Ismat showed in [6], for example, how gamification in a university engineering course enhanced student engagement, grades, and satisfaction. It is results such as these that sharply capture the transformative potential of integrating such technologies.

According to the self-determination theory (SDT), the fulfilment of three key innate psychological needs—autonomy, competence, and relatedness—lies at the core of this investigation. The use of telepresence robots fuels autonomy in that students gain control over their learning experience: to virtually move themselves around and immediately interact with peers and instructors. Gamification addresses competence in that there are clear goals, the tracking of progress, and adaptive challenges consistent with the abilities of each student [7,8]. This is in regard to the relatedness created through the social interactions afforded by telepresence robots, whereby the remote students feel more connected to peers and a learning community. In applying these theories, an immediate consequence was evident in the specific methodologies applied within this study. For instance, the experimental methodology was informed by constructivism through the promotion of interactive, hands-on learning experiences facilitated by the telepresence robots. Meanwhile, the SDT provided the framework for designing motivational elements in the gamified modules whereby students felt autonomous, competent, and connected.

2. The Literature Review

The literature review section first highlights the knowledge gap this study addresses in exploring an unexamined but promising synthesis of telepresence robots and gamification. This paper then clearly states the core methodologies for how this combination can amplify engagement. The results and their respective discussion are explained in the proceeding sections. The conclusion with the future directions are discussed at the end of the article.

The concept of telepresence robots has evolved significantly over the past few decades, with rapid advancements in robotic technologies enabling more immersive and realistic remote experiences. Early telepresence systems were restricted to simple video conferencing tools that provided audio and visual communication but no physical embodiment at the remote site [9]. However, integrating mobility and autonomy in robotics opened new possibilities for telepresence applications.

One of the pioneering telepresence robots was the Telepresence Mobile Robot, which was developed in 1993 and consisted of a computer screen mounted on a motorized base that could be steered remotely through a simple interface [10]. This established the basic framework of modern telepresence robots as mobile robotic platforms that enable users to inhabit and navigate remote environments virtually. In a similar way, in the year 2002, the Pioneer 3 robot exhibited advanced telepresence features, this time operating over high-speed wireless networks. The telepresence features included, among others, real-time video and audio transmission, and semi-autonomous navigation under the control of the remote operator [11]. These robots were put on the market and targeted for workplace use to provide remote employees with more physical immersion and mobility than stationary video conferencing systems [12].

Modern telepresence robots incorporate high-definition cameras, microphones, mobility controls, and software enabling realistic presence across distances. Steps have been taken towards more autonomous navigation and accessibility features catering to diverse users [13]. The use of telepresence robots in academic institutions is still in the early stages, but initial studies have affirmed their value in enhancing learning experiences. One of the first educational applications was developing a remote laboratory using robotic telepresence, allowing students to conduct physics experiments remotely as if physically present [14]. The interface enabled students to view the experiment workspace through a mounted camera and use robotic arms to manipulate equipment while receiving sensor feedback. This expanded practical lab opportunities for distant students.

At the University of Lahore, telepresence robots developed by Naseer et al., [15] made it possible for doctors to take part in the local diagnosis of a patient from any part of the world. The doctors could control the robots through the system, move around the hospital, communicate with patients in the wards, and ask questions. Aside from attendance, creative research has delved into using telepresence robots for experiential learning. Tan Q in [16] created virtual field trips in which students used a robot not only to attend laboratories but also to participate in experiments and with their educators. Students gained immersive exposure to new environments without leaving school.

The theoretical basis for gamification draws on motivation psychology and principles that drive game enjoyment. Kovácsné Pusztai in [17] identified three primary factors that make games intrinsically motivating: challenge, curiosity, and fantasy. The challenge involves game goals with uncertain outcomes that appeal to human desires for mastery and achievement. Gamification applications are widespread across the business, fitness, productivity, and education sectors. In academia, gamification aims to infuse education with the stimulating and addictive qualities of games that sustain student attention and enjoyment. Proponents argue that it provides learners with clear goals, ability-adaptive challenges, rapid feedback cycles, storytelling, and social engagement opportunities [18]. This fosters their intrinsic motivation to learn instead of relying on external pressures and incentives.

However, critics have argued that gamification strategies premised solely on points, badges, and leaderboard systems often fail to bring deep engagement. The authors in [19] cautioned that poorly designed gamification results in “measurement fixation” and diminishing returns from external rewards. Meaningful gamification requires enriching learning activities with true gameplay qualities and narratives that provide students with agency over quest-like journeys matched to their ability levels [20]. A growing body of empirical research has explored the implementation and effectiveness of gamified learning. An early study examined the academic impacts of using game elements in an undergraduate business course [21].

In the context of digital and e-learning platforms, the authors in [22,23] investigated the effects of badges on students’ motivation and achievement in an undergraduate psychology course. Beyond quantitative learning gains, researchers have also qualitatively evaluated the affective and motivational impacts of gamified education. The authors in [24] investigated student perceptions of game elements by surveying undergraduates taking language courses supplemented with points, leaderboards, and achievement badges. The responses indicated that students found gamification enjoyable [25] and believed that it improved their learning motivation. Badges provided a goal progression framework [26], and points-based feedback was valued. However, competitive elements were not uniformly preferred, requiring careful integration.

The growing adoption of learning technologies in education [27] has spurred significant research on effective modalities for technological integration. Early perspectives viewed technology tools as merely supplementary [28] add-ons to conventional teaching. However, Wong in [29] argued that this fails to harness the transformational capabilities of technology. Meaningful integration requires moving learners from passive to active roles while elevating technology to a central position in the learning process.

The authors in [30] formulated the TPACK framework, which describes optimal technology integration as understanding interactions between content knowledge (CK), pedagogical knowledge (PK), and technological knowledge (TK). Instructors must cultivate technology-enabled pedagogical content knowledge or TPCK to deliver engaging education. Similarly, Bobich and Mitchell in [31] advocated a holistic approach where technology simultaneously transforms curricular resources, activities, and assessments rather than just one aspect.

Emphasizing active learning, Garnham in [32] found that technology is most impactful when students use it as a cognitive tool in authentic construction, design, and problem-solving activities rather than simply receiving content through technology. This aligns with prior results by Agarwal in [33], showing the most significant educational gains when technology provides students with cognitive control and self-directed learning opportunities.

However, schools often struggle with meaningfully assimilating emerging technologies within instructional models. The authors in [34] explore emerging technology in the metaverse regarding the education domain. Automated Assessment and Feedback in Higher Education Using Generative AI was discussed in [35] by Naseer et al. Thus, studies have stressed that active learning pedagogies, teacher development, and whole-curriculum perspectives are vital enablers of educational technology [36].

In summary, the existing literature highlights that telepresence robotics and gamification have demonstrated value in enhancing learning when applied individually. However, their synergistic combination and implementation from a holistic curriculum-wide lens remains unexplored. Guiding frameworks emphasize that technology should amplify active learning and student engagement. The proposed research aims to deliver this goal by investigating the integration of telepresence robots and gamification to reimagine engaging education for all students.

3. Methodology

3.1. Research Design and Approach

The study uses a mixed-method approach for the combined use of quantitative and qualitative techniques to give a multidimensional insight into the impacts of telepresence robotics and gamification on student engagement. Mixed methods use the strengths of both the quantitative and qualitative paradigms in the study of complex phenomena as a means of attaining a comprehensive understanding of the phenomena under study [37].

The research is designed around the classroom-based experiments that compare engagement and a difference in perception across the student cohorts undertaking gamified learning modules delivered through either a standard computer/tablet delivery medium or through a telepresence robot.

For example, engagement observables at the behavioral level quantify indicators such as class participation rates, assignment completion times, and scores, on the one hand; learning analytics, on the other hand, capture similar indicators. Qualitative methods will consist of surveys, interviews, and focus groups to obtain fine-grained data on student attitudes, motivation, enjoyment, and the overall learning experience with the different platforms. Triangulating these quantitative and qualitative strands thus provides a comprehensive investigation into the affective and behavioral impacts of telepresence robotics and gamification on student engagement.

The study, therefore, mirrors a call for the design of empirical work with emerging learning technologies to go beyond mere descriptive studies to include controlled experimental investigations comparing technological interventions of interest against standard conditions [38]. A mixed-method approach to striking a balance between robust datasets and personalized voices is also consonant with frameworks for the holistic evaluation of technology in education postulated by experts, such as that elaborated upon in [39].

The proposed model of integrating telepresence robots with gamified learning for enhanced student engagement is illustrated in Figure 1. This model outlines the key stages, including participant selection, telepresence robot interaction, gamified learning engagement, and data collection and analysis, providing a structured framework for the study’s methodology.

3.2. Telepresence Robot Design

A key early process in the research design is the collection and construction of classroom-specific, custom telepresence robots, as shown in Figure 2. The following state diagram is graphical and demonstrates the states of the algorithm. The first state is the “Selection of Participants”, where the student sample populations are selected from the university districts using stratified random sampling. The two parallel states that immediately follow are the “Telepresence Robot Interaction” and “Gamified Learning Module Engagement”. In the “Telepresence Robot Interaction”, the telepresence robots have been arranged for each student, process commands, and interact in their environment. Meanwhile, the output progresses under “Gamified Learning Module Engagement”, in which each student sets the progress of the gamified module and advances in the presentation of content and updating of progress based on engagement and responses until the module is complete.

These two states converge on data collection and analysis, where quantitative and qualitative data for both students are collected and analyzed: scores for engagement, completion rates, feedback, and interview data. The totality of collected data all aggregates to reach the final state of the “Engagement Data and Study Findings”, for a thorough analysis that ends with the delivery of engagement data and study findings. It is quite evident, then, that the algorithm process flow gives an overall idea of the flow and interplay of these states.

To create an immersive user experience, the robots incorporate several features, as illustrated:

• A camera with a 110° field-of-view and microphones enabling 360° sound capture to mimic natural sight and audio perspectives.
• A height-adjustable robotic arm with a gripper allows remote students to grasp and manipulate objects.
• Touchscreen display which tilts vertically for standing viewing and pivots for directional control.
• Obstacle avoidance and autonomous navigation capabilities using LIDAR and ultrasonic sensors.
• Wireless connectivity over 5G cellular networks to minimize lag and support real-time control.
• Emergency stop buttons, anti-tip sensors, and redundancy brakes for safety.
• Audiovisual software to manage camera feeds and allow remote students to personalize their on-screen avatar.
• Modular engineering allows customization of the sensor, manipulation, and mobility capabilities.

3.3. Software Description

The physical capabilities of such robots are powered by a sturdy software architecture: an audiovisual management system with integrated high-definition camera feeds, directional microphones, and on-screen avatars that can be customized to suit the taste of a user in terms of creating an immersive and personalized experience. The navigation software employs sophisticated algorithms in processing LIDAR and ultrasonic sensor data to allow for smooth autonomous navigation and obstacle avoidance in dynamic classroom environments.

These robots are integrated with gamified learning platforms, which in turn allow for real-time synchronization between physical and digital interactions. The adaptive learning algorithms that tailor content delivery, based on the progress at an individual speed in the interface, afforded intuitive object manipulation of the robotic arm during collaborative tasks. Wireless firmware updates will keep the software current, and thus scalable and flexible across diverse educational applications.

These functionalities maximize students’ sense of immersion and agency when learning through telepresence robots. The robotic arm increases possibilities for tangible participation in group activities involving hands-on materials. Collaborations with roboticists and engineers from local universities will enable rapid design prototyping and user testing to refine the telepresence platform over multiple iterations until optimal overall utility is achieved.

Figure 3 illustrates a telepresence robot in a university classroom, exemplifying the application of our research on integrating telepresence robots with gamification to enhance student engagement. This provides a glimpse into our study’s findings, where such technology increased participation, among other things, indicating more efficient learning processes. The telepresence robot symbolizes our innovative and novel approach to inclusive education, offering remote or physically challenged students a sense of belonging and active participation in the learning experience.

3.4. Selection of Participants and Sampling Methods

The experiments involve students from six university districts that reflect diversity in learner demographics, academic performance, socioeconomic backgrounds, and geographic locales. Stratified random sampling was utilized to recruit student participants across:

• Urban, suburban, and rural districts
• High-, medium-, and low-income neighborhoods
• Ethnically and linguistically diverse populations

Table 1. Participant demographics.

Parameter	Computer Group	Telepresence Robot Group
Sample Size	150	150
Gender Ratio (M/F)	1:1	1:1
Age Range	18–26 years	18–26 years
University Semester Levels	1st year, 2nd year, 3rd year	1st year, 2nd year, 3rd year
Neighborhood Income Profile	33% Low, 33% Medium, 33% High	33% Low, 33% Medium, 33% High
Prior Academic Performance	33% Below Avg, 33% Avg, 33% Above Avg	33% Below Avg, 33% Avg, 33% Above Avg

summarizes key demographic parameters of the 150 student participants in the control group using computers and 150 students in the experimental telepresence robot group. The stratification approach ensured both groups had balanced gender ratios and age ranges, spanning the 1st semester to the 3rd semester, with equal representation of low-, medium-, and high-income neighborhoods, and comparable prior academic achievement levels. This enabled a standardized comparative analysis between the two modalities.

This sampling approach ensures that the findings encapsulate responses across diverse student subgroups rather than reflecting the experiences of a narrow demographic segment. Each district’s classrooms were randomly assigned as control groups undertaking gamified modules using standard computers/tablets or experimental groups using telepresence robots. All participants complete informed consent protocols with parental permissions obtained for minors.

The study sample comprised 450 students—150 1st semester university pupils, 150 2nd semester university pupils, and 150 3rd semester university pupils. Within each education level sub-group, 100 students were randomly allocated to the control condition without telepresence robots, while 50 experienced the modules via robotic telepresence. This distribution enables comparative analysis both across age groups and between technological modalities. The total sample size provides statistical power to detect meaningful effects on engagement indicators.

3.5. Description of Telepresence Robots and Gamified Modules

The study centers around a curriculum of gamified learning modules spanning various subject areas—math, science, history, language arts, geography, and creative arts. Modules are designed following established best practices for education gamification focused on providing students with clear quests and goals, adaptive challenges, progression via points/badges, rich narratives and worlds, avatars to represent themselves, and opportunities to unlock rewards [40].

For example, the math module takes students on quests to defeat enemies by solving puzzles and advancing through digitally rendered worlds. The science module allows students to create avatars who are researchers investigating outbreaks and they have to solve mysteries collaboratively. The language arts module enables students to inhabit fictional worlds from books and engage with scenarios and characters.

Each subject module includes a sequenced series of gamified content and activities. Modules are calibrated to the respective grade-level curricula and tailored to be completed within 10–15 h. Learning analytics track students’ progress through the cascading activities and collect data on participation, completion, and competence in each component (Appendix C).

In the control group, students use standard keyboard and touchscreen controls to access the gamified content on computers/tablets. In the experimental condition, students instead use the custom telepresence robots designed for the study. They can navigate the physical classroom environment through their robots, interact with instructors and peers via the robotic avatar, and use the robotic arm to manipulate any hands-on elements. Network capabilities enable breakout activities in separate rooms. The platform brings gamified learning alive, merging digital experiences with robotic telepresence in real-world classrooms.

3.6. Data Collection Methods

Quantitative and qualitative data are gathered to assess student engagement outcomes with the different learning modalities. Quantitative metrics collected for comparative analysis include the following:

• Participation rate: percentage of learning content/activities started and completed.
• Completion time: average time taken to complete gamified modules end-to-end.
• Scores: percentage of correct responses on knowledge assessments integrated within modules.
• Learning analytics: platform interaction data on areas like time spent, pacing, and behaviors, plus eye-tracking via telepresence robots.

Alongside these performance indicators, questionnaires and embedded feedback surveys gather qualitative perceptions from students on the following (Appendix A):

• Enjoyment, interest, and overall experience with the learning modules.
• Ease of use and areas for improvement in telepresence robot platforms.
• Sense of immersion, involvement, and connection when learning through the robot compared to a computer.
• Comfort interacting via the robotic avatar with instructors and fellow students.

Additionally, in-depth interviews and focus-group discussions with 10% of participants elicited detailed perspectives on telepresence-robot-enabled gamified learning [41]. Focus groups were conducted with groups experiencing standard computers and those using robotic telepresence to enable comparative insights. Semi-structured interviews probed participant attitudes, motivation levels, engagement enablers and barriers, and recommendations. Integrating deep learning techniques for higher education represents an upcoming and required tool in this 21st century era [42]. Durán and Fuentes explain the enhancement of artistic learning in university pupils to generate a creative mindset by using the gamification techniques [43]

The multifaceted quantitative and qualitative data provide a comprehensive 360-degree understanding of how the fusion of telepresence robots and gamification impacts tangible engagement outcomes and underlying motivations, perceptions, and learning processes. Integrating metrics on participation, performance, and affect illuminates both the observable and subtler influences on the student experience. Methodological triangulation strengthens the findings’ reliability and contextual richness.

This rigorous mixed-method approach examining student participation with telepresence robotics and gamification through an experimental comparative lens enables a well-rounded investigation of this potentially transformative educational innovation. The custom telepresence platform created for the study provides students with an optimized embodied experience during gamified learning. The findings aim to provide technologists, researchers, and educators with actionable guidance on integrating emerging technologies to drive more stimulating, inclusive classrooms and reimagine education for the digital age.

A power analysis was conducted to ensure a sufficient sample size for detecting medium effect sizes. The sample was stratified across demographic factors such as gender, socioeconomic status, and prior academic performance to ensure the study’s findings are representative of diverse student populations.

3.7. Qualitative Analysis Tools

The qualitative data for this study were collected through semi-structured interviews and focus group discussions with both the telepresence robot group and the control group. A thematic analysis approach was applied to extract key insights from the student experiences, perceptions, and feedback related to engagement and motivation. The following are the detailed steps for qualitative data analysis, and these are illustrated in

Table 2. Qualitative data analysis approach.

Stage	Process	Tools Used
Data Collection	Semi-structured interviews and focus groups with 10% of participants	Digital audio recorders
Transcription	Verbatim transcription of interviews	Transcription software v12
Coding	Hybrid approach of inductive and deductive coding	NVivo software v12
Inter-Rater Reliability	Cross-coding by two researchers; discrepancies discussed and resolved	Cohen’s Kappa (0.80 agreement)
Theme Development	Grouping codes into broader themes related to engagement, motivation, technical challenges, and autonomy	Manual and NVivo
Triangulation	Cross-referencing qualitative findings with quantitative metrics	NVivo v 12, quantitative data

.

• Data Collection
  • Semi-structured interviews and focus groups were conducted, lasting between 45 and 60 min for each session (Appendix B).
  • A total of 10% of the participants from both the telepresence robot group and the control group were selected for the interviews (15 students from each group).
• Transcription
  • All interviews and focus-group discussions were transcribed verbatim using transcription software, ensuring that no verbal content was missed, including pauses, hesitations, and key non-verbal cues (where applicable).
• Coding Process
  • A hybrid approach was employed where both inductive (data-driven) and deductive (theory-driven) coding methods were used. Inductive codes emerged directly from the data, while deductive codes were based on predefined concepts such as “student motivation” and “engagement levels”,
  • NVivo software was used for the coding and categorization of data. This software facilitated the organization of large volumes of qualitative data and allowed for efficient theme identification.
• Inter-Rater Reliability:
  • Two independent researchers performed the initial coding of the transcripts. This process was followed by a reliability check, where the coded transcripts were compared. Discrepancies between the two researchers were discussed and resolved to ensure consistent interpretation and reduce subjective bias.
  • A Cohen’s Kappa coefficient of 0.80 was achieved, indicating a strong level of agreement between the two coders.
• Theme Development
  • The coded data were grouped into broader themes, such as a “sense of presence”, “autonomy in learning”, and “technical challenges”, which were prevalent in both groups but varied in intensity depending on the learning modality (telepresence robot vs. traditional computer-based learning).
• Triangulation
  • In addition to coding, triangulation was employed by cross-referencing qualitative insights with the quantitative data (participation rates, task completion times, etc.). This approach strengthened the validity of the findings by ensuring that results were consistent across different data sources.

3.8. Rationale for the Sample Size

The selection of 150 students per group (telepresence robot group and control group) was guided by both statistical and practical considerations. The sample size was determined through a power analysis to ensure that the study would have sufficient power to detect meaningful differences in the key outcome variables such as participation, completion rates, and engagement levels.

Participants were assigned to either an experimental or a control group, each with a stratified random sample of about 150 students, ensuring that the composition of these groups was well-balanced in terms of year of study, socio-economic background, and prior academic performance. Though the groups were matched on paper, minor variations in actual composition did arise but were weighted accordingly in the statistical analysis to enable more robust comparisons.

Justification for Sample Size

• Power Analysis

  ○

  A power analysis was conducted to ensure the ability to detect medium effect sizes (Cohen’s d = 0.5) with a power of 0.80 and a significance level (α) of 0.05. The target sample size was set to provide adequate power for the primary quantitative comparisons between the telepresence robot group and the control group.

• Stratified Random Sampling

  ○

  The use of stratified random sampling ensured that the sample was representative of the diverse student population, including variations in gender, socioeconomic background, academic performance, and geographic location.

  ○

  The sample was stratified based on several key demographic factors to reduce bias and ensure comparability between the groups, as shown in

  Table 3. Stratification criteria for sample selection.

  Stage	Process		Tools Used
  Gender (M/F)Ratio	1:1	1:1	Ensures balanced representation of genders across groups
  Socioeconomic Status	33% low, 33% medium, 33% high	33% low, 33% medium, 33% high	Captures engagement differences across income levels
  Academic Performance	33% below avg, 33% avg, 33% above avg	33% below avg, 33% avg, 33% above avg	Ensures comparability across varying academic achievement levels
  Geographic Distribution	Urban, suburban, rural	Urban, suburban, rural	Reflects diversity in educational access and engagement based on location

  .

• Sample Size for Subgroup Analysis

  ○

  The stratification allowed for a subgroup analysis to compare engagement metrics across different student demographics (e.g., low-income vs. high-income students). This enhances the study’s generalizability by ensuring that the findings are applicable to a diverse student population.

• Balance Between Statistical Rigor and Logistical Feasibility

  ○

  While larger sample sizes would increase precision, the chosen sample size of 150 per group strikes a balance between logistical feasibility (in terms of time, resources, and robot availability) and the need for a robust statistical analysis. This sample size also allows for meaningful subgroup comparisons without overburdening resources.

4. Results

4.1. Analysis of Student Engagement Metrics

The comparative analysis of engagement metrics between the control group undertaking gamified learning on computers versus the experimental group using telepresence robots revealed significant differences in participation and performance.

Table 4. Engagement metrics of pupils.

Metric	Computer Group	Telepresence Robot Group	% Change
Participation Rate	75%	95%	+26%
Completion Rate	60%	90%	+50%
Completion Time	12 h	9 h	−25%
Assessment Scores	80%	85%	+6%

summarizes the critical quantitative engagement indicators measured in the study across the computer and telepresence robot groups. The telepresence cohort showed sizeable improvements in all metrics, such as 26% higher participation, 50% better completion, 25% reduced time, and 6% higher scores. This affirms the positive impact of telepresence robots on gamified learning engagement.

Figure 4 shows that across the age groups, the telepresence robot cohort showed markedly higher average participation rates in commencing gamified modules (95%) compared to the computer-based group (75%). The robot cohort also demonstrated elevated completion rates, with 90% fully finishing modules end-to-end versus 60% completion in the control group. These results along with the module completion rate by the group, as shown in Figure 5, substantiated the findings, if the utilization of telepresence robotics increases motivation for active student participation in the gamified educational activity, which should cause the initiation and persistence rates to increase in those integrated sequences.

Additionally, Figure 6 shows that the average module completion time by the robotic telepresence group was 25% faster than the computer cohort. It is evident that the efficiency of engagement allowed by a telepresence modality is greater with students moving through gamified material at faster rates while still achieving understanding. Other learning analytics for pause frequency and pacing also demonstrated more consistent potential by the experimental group.

Knowledge checks by the two groups at various stages of the modules returned some minor differences in scores; however, the telepresence group showed a slightly better mean accuracy (85%) compared to the computer group (80%), as shown in Figure 7. At the same time, the qualitative feedback indicated students’ perceptions of the telepresence modality of creating better environments for focus and soaking in information because of fewer on-screen distractions.

Quantitative measures through participation and performance all affirmed that significant uplifts occurred for student engagement and learning from gamified learning occurring with telepresence robots versus computer-only based approaches. Robotic telepresence amplifies the psychological investment in module participation while facilitating ergonomic engagement efficiency, as shown in Figure 8.

4.2. Impact on Diverse Student Groups

Further mining the engagement metrics among student subgroups revealed positive implications for how telepresence robotics democratizes gamified learning. Students living in low-income neighborhoods with less access to technology were 80% engaged when interacting with telepresence robots, in contrast to just 40% of those in the computer control group. This implies that the new medium of immersion gave significant added support to the otherwise low-motivation, self-directed, digitally educated, disadvantaged students. English-language learners acquiring access through robotic telepresence likewise exhibited participation and completion rates greater than 85%.

Students with learning disabilities and special education needs also showed much-improved engagement using the telepresence robot method compared to using regular computers. Interview data revealed that these sensory and social robot experiences helped in maintaining students’ engagement and interest, hence overcoming two common barriers in online learning—information overload and isolation.

Although there were marginal differences across prior achievement levels, telepresence robots were broadly accessible and, in general, improved gamified learning engagement for groups commonly underserved in both traditional and technology-based education. Embodied physical agency combined with the curation of the experience makes it apt for inclusively engaging, tailor-made learning to varied learner profiles.

4.3. Qualitative Feedback and Observations

The interviews and focus groups provided qualitative insight into uplifts in engagement and motivation, enjoyment, and perceived learning gains that were quantitatively observed with the telepresence robot modality. The most prominent theme that rose from this type of approach was the feeling of immersion and inhabitation enabled by the robots. Students reported feeling more actively ‘present’ in gamified teaching scenarios and hence more invested in what was happening. The capability to move around the classroom and manipulate objects increased the perceptions of autonomy and ownership of the experience.

Table 5. Qualitative Feedback Themes.

Theme	Sample Responses
Embodiment	“Felt like I was actually there”, “It was like being in the game”,
Immersion	“Got absorbed in the story”, “Forgot I was even using a robot”,
Social Connection	“More natural talking to classmates through robot”, “Collaborating was smoother”,
Sensory Engagement	“Moved around a lot so it stayed interesting”, “Could focus better”,
Enjoyment	“Way more fun than just staring at a screen”, “It felt like an adventure”,

gives a summary of key qualitative response themes in relation to the interviews and focus groups with students. The responses highlighted benefits from the telepresence robots for supporting embodiment, immersion, social connection, sensory engagement, and enjoyment. Consistently, the students also noticed extra social connection and higher collaboration with the telepresence robots. The communication with the instructors and amongst the students became quite natural and easier with the robot embodiment in comparison to the computer screens. Gestures, body language cues, and non-verbal responses increased rapport.

Another incidental finding was that the telepresence robots delivered quality sensory stimulation, sustained student interest, and kept energy levels optimal even for the most prolonged gamified module. The multisensory experience provided by the combination of mobility, vivid audiovisuals, and robot gravitas was something that computers lacked. Some students did report an occasional technical lag but were truly interested.

Indeed, participants highlighted that the gamified learning through telepresence robots makes it not an abstract digital experience but an embodied social journey. You are physically able to step into a role and pursue a quest through the robots, which really makes a huge difference from flat paper in a typical unit. This outlines the synergy existing between robotic telepresence technologies and curricular gamification in increasing student engagement.

5. Discussion

5.1. Interpretation of Results

These findings provide strong evidence to prove that the incorporation of telepresence robots with gamified learning strategies go a long way in enhancing student engagement and motivation toward a particular subject, leading to a performance increase. Quantitatively, participation rates surpassed 90%, while the completion time reduced by more than 25% and module completion rates were 30% higher compared to the normal methodology. These findings agree with the previous literature indicating how gamification can effectively be used to assure active learning and student motivation [6]. Such effects, however, seem to be amplified because robotic telepresence in combination with game-based learning appears to offer quite immersive and interactive experiences that create deep psychological investment and multisensory engagement. This corroborates such theoretical frameworks as constructivism and the self-determination theory, which claim that a physically active, autonomous, and social learning environment is apparently crucially needed [44].

5.2. Telepresence Robots and Inclusivity in Education

Most noteworthy of all in this article are the positive effects on populations of students who are often at a disadvantage: students from lower socio-economic backgrounds, ELLs, and students with disabilities. For example, low-income students were found to be engaged 80% of the time when using telepresence robots, whereas they were engaged 40% of the time when they used computer-based controls. These findings are supported and reinforced by the authors in [45], who identified how new technologies have the potential to democratize high-quality education. The robots allowed ELLs to obtain multimodal sensory input that supported the processing and also provided them with a medium by which they could communicate, hence supporting those previous studies indicating that multimodal scaffolding is among the ideal methods of enabling language learners to learn the syntax and semantics of any target language, as pointed out in [46].

Similarly, students with disabilities benefited from spaces that were customizable and collaborative, reducing the feelings of isolation often experienced in traditional learning or online spaces. This underlines the contribution of telepresence robotics to create inclusive, participatory learning environments aligned with global goals for equity in education.

5.3. Implications for Student Learning and Motivation

These findings also have important implications for understanding and improving student motivation. Reports of heightened feelings of autonomy, competence, and relatedness with robotic-assisted gamified learning are coherent with the self-determination theory, placing these psychological needs as critical for sustained motivation in education. Telepresence robots accommodated active and participative learning through students’ capability to enact and simulate knowledge in roles and hands-on games. This is in line with the work of the authors of [47], who showed that gamified experiences foster intrinsic motivation and engagement. These motivational benefits, together with the possibility to merge digital interactivity with physical presence, suggest that telepresence robotics may catalyze a paradigm shift in learning environments from passive information consumption towards active knowledge construction.

5.4. Comparisons with Traditional Educational Methods

Overall, the quantitative results showed participation and completion rates for gamified learning transported from a baseline of about 20–25% with traditional, computer-only educational environments to over 90% using telepresence robots. The perceptions indicated increased embodiment, focus, and collaboration compared with the typical classroom experience.

This contrast seems to underline how different types of learning may potentially be activated with emerging technologies, like, for example, telepresence robots, from those receiving traditional teacher-centered education. The immersion that passive listening to lectures affords is very much weaker compared to that of students who feel they are literally taken somewhere through the historically contextualized stories given to them by their robotic representatives.

As is shown in Figure 9, the telepresence robot group’s grades increased from around 72 in the first month to nearly 90 by the sixth month, showing an improvement of approximately 18 grade points over the 6-month period. The computer group’s grades started slightly lower at around 70 in the first month and rose to roughly 88 by month 6, an improvement of about 18 grade points as well. While both groups saw substantial grade increases, the telepresence robot group maintained a lead of around 1.5 to 2 grade points over the computer group across all time points.

As is shown in Figure 10, Teacher 1 gave a satisfaction rating of approximately 76 out of a presumed maximum of 100. Teacher 2 had the lowest satisfaction rating among the four teachers, scoring around 52. Teacher 3’s rating was the second highest at roughly 82. Teacher 4 gave the highest satisfaction rating, which appears to be around 84 or 85.

However, it was also gathered that barriers in the use of telepresence solutions are the costs of tools, technical reliability, and other specialized instructional designs that would need to be brought in to create maximally immersive learning. Further systematic testing of telepresence and gamification techniques against a full spectrum of conventional methodologies, using comparative benchmarks, is sorely needed. Informed evaluations can help the introduction of new tools in balanced ways with time-tested pedagogies in the future of education.

Figure 11 shows the relationship between student engagement levels (on the x-axis) and their corresponding learning outcomes (on the y-axis). The data points are scattered across the plot, but there appears to be a general positive correlation, meaning that higher engagement levels tend to be associated with better learning outcomes. At the lower end of engagement levels around 70–75, the learning outcomes are clustered in the 65–80 range, indicating relatively poorer performance. As engagement increases from around 80 to 90, the learning outcomes also improve, spanning roughly the 75–95 range with more data points concentrated in the 80–90 region. For engagement levels above 90, most of the data points have learning outcomes above 90 as well, with several instances of very high outcomes near 100. This suggests that students with high engagement tend to achieve excellent learning results.

This is, after all, an exploratory study with initial evidence pointing to the strong relative edge of immersive technologies, including that of telepresence robots, over conventional learning media, modalities, and spaces. Other controlled studies will place telepresence and scenario-based reflection, together with gamification approaches, on a continuum of conventional educational methods, thereby bringing still more insights into shaping hybrid learning ecosystems.

While the results indicate a strong potential for enhancing student engagement, scaling the use of telepresence robots across institutions poses challenges. Future studies should explore cost-effective solutions and teacher training programs to ensure successful and sustainable implementation in diverse educational contexts.

In consequence, future research should expand the scope of the comparative analysis beyond telepresence robots by including other forms of immersive learning technologies, such as virtual and/or augmented reality or interactive smartboards, in their curricula. This will enable researchers to put telepresence robots within the context of a broader digital ecosystem of learning tools while indicating the relative strengths and weaknesses of the robots concerning other technologies. This expanded scope will provide a fuller understanding of how the diversity of technologies can be used to improve the opportunities for engagement and learning by students and will guide educators and institutions in the choice of the most relevant tools for their particular educational context.

6. Conclusions

This pilot study generated strong evidence demonstrating that the inclusion of telepresence robotics with gamified learning can significantly increase student engagement in comparison to tradition-based methods. This includes twofold increases in participation/completion rates, 25% reduced completion times, and increased embodiment and motivation measured through telepresence robots. The interpretation of the qualitative results indicates that gamified learning enhances in-depth psychological investment, sensory experiences, social relationships, and dramatic embodiment with the telepresence of the robots. This accounts for the observed increased engagement as seen through quantitative measurements. Among the several recommendations is the call for technical improvements in the responsiveness of controls and the related Internet connectivity to enhance the telepresence. Despite the aforementioned technical difficulties, the, results of this study suggest promising learning gains for this under-privileged population. This research is important because it suggests a novel experimental design that showcases a contribution to the research on telepresence robotics in K-12 education. The elucidation of the empirical evidence on such synergy between robotic telepresence and gamification toward the augmented pedagogy is outstanding. Follow-up studies can now exploit these insights that are acting like catalysts for better innovations in learning technologies. Exciting directions for future studies include investigations of the long-term curricular impact, scale implementation, and progress in complementarity with project-based learning, and the examination of social–emotional effects. As telepresence robotics mature, their implications for the philosophy and culture of emerging learning ecosystems merit examination. While this study does point out certain important possibilities concerning the integration of telepresence robotics with gamified learning, there are issues to be sorted out, including the high costs of implementation, technical reliability, and specialized teacher training is required. Also, since this is a pilot study, the findings are context bound and need to be further validated in different educational settings. Further research should compare the effectiveness of telepresence robots with other immersive technologies, such as virtual and augmented reality, to determine their relative strengths and weaknesses in different educational contexts.