IoT Devices and Their Impact on Learning: A Systematic Review of Technological and Educational Affordances

Abstract

A principal factor of the fourth Industrial Revolution is the Internet of Things (IoT), a network of “smart” objects that communicate by exchanging helpful information about themselves and their environment. Our research aims to address the gaps in the existing literature regarding the educational and technological affordances of IoT applications in learning environments in secondary education. Our systematic review using the PRISMA method allowed us to extract 25 empirical studies from the last 10 years. We present the categorization of educational and technological affordances, as well as the devices used in these environments. Moreover, our findings indicate widespread adoption of organized educational activities and design-based learning, often incorporating tangible interfaces, smart objects, and IoT applications, which enhance student engagement and interaction. Additionally, we identify the impact of IoT-based learning on knowledge building, autonomous learning, student attitude, and motivation. The results suggest that the IoT can facilitate personalized and experiential learning, fostering a more immersive and adaptive educational experience. Based on these findings, we discuss key recommendations for educators, policymakers, and researchers, while also addressing this study’s limitations and potential directions for future research.

1. Introduction

The term “Internet of Things” (IoT) was first used in 1999 by the British technology expert Kevin Ashton to describe using RFID (Radio-Frequency Identification) technology to connect devices over the Internet, enabling the collection and exchange of data without human intervention [1]. Some examples of IoT devices used in fields like education, research, and health include smart thermostats, lighting systems, cameras, wearables, and sensors for monitoring moisture and soil quality. Since then, the IoT has expanded to cover a broader range of technologies and applications, connecting smart objects and physical devices to the Internet. Khanna and Kaur [2] estimated that the number of smart objects—excluding tablets, computers, and mobile phone-connected devices—reached 75 billion in 2025. According to García et al. [3], IoT devices are categorized into two main types: (a) smart objects, also known as Intelligent Products, which are physical elements identifiable throughout their lifecycle and capable of interacting with their environment and other objects, and (b) non-smart objects, such as sensors and actuators, which lack intelligence. Sensors measure physical parameters—for example, photoresistors measure light and thermistors measure temperature, while actuators perform actions on themselves or other devices, enabling specific functionalities.

Schools from primary to secondary education, as well as academic institutions, are increasingly integrating IoT applications into educational activities to create a modern and efficient learning environment that aligns with the demands of contemporary society. IoT-based technologies enable interactive communication and transform educational environments into smart spaces [4]. These smart environments leverage advanced technologies to provide more interactive, personalized, and efficient learning experiences. They can detect changes and adapt to the needs of various teaching and pedagogical approaches, ultimately improving the learning process [5,6]. One significant advantage of using the IoT in education is its ability to collect and analyze real-time student performance and behavior data. This data allows for personalized teaching approaches, enhancing motivation and contributing to the achievement of learning outcomes [7]. Research indicates that students learn faster when they actively engage with relevant activities, where technology plays a crucial role [8,9,10]. The IoT is viewed as a revolutionary element in education, fostering innovative learning environments and transforming traditional teaching and learning strategies with conventional tools. The IoT enables the development of interactive and immersive learning experiences and promotes the seamless integration of technology into educational settings [11,12].

1.1. Other Systematic Reviews

Two recent studies reported on introducing and utilizing IoT devices in education. On the one hand, Piccolo et al. [13] focused on some potential roles of user-centered technology, more specifically of Do It Yourself (DIY) projects, in order to enrich students’ knowledge, while enabling them to better understand their own working context by setting specific objectives. Students planning DIY projects with the IoT utilize embedded systems, sensors, software, and other technologies to measure and understand physical phenomena. The main research questions concerned the role of education in the IoT, the characteristics of the IoT, the tasks performed using these technologies in an educational context, and the challenges regarding the transformative power of students. Their research led them to develop a set of guidelines for selecting an appropriate hardware platform and determining the adequacy of data collected in the field, considering different locations, times of day, and seasons. However, with the readiness, skills, and previous knowledge of the students in mind, an educational strategy was designed to maximize their participation.

Espinosa-Dublar’s [8] research focused on studies published from 2010 to 2021 and examined the impact of the integration of emerging technologies (the IoT, AI, e-learning, etc.) on the knowledge and skill acquisition of K-12 students in the Philippines. The purpose of this systematic review was to provide an overview of the current impact of the integration of emerging technologies on the educational process and how they contribute to students’ knowledge and skill development. This research aimed to identify the types of emerging technologies used, the effects of their integration, and the factors that influence their effectiveness. As it emerged, the integration of emerging technologies, such as virtual and augmented realities, the Internet of Things, machine learning, and online platforms, seemed to promote and enhance learning by motivating students to participate. However, efforts are needed to address the challenges that have been identified, such as inadequate infrastructure and incomplete teacher training.

In conclusion, these systematic reviews do not report on the educational and technological affordances of IoT and how they contribute to effective learning. The smart devices used and resources per task and field of application are not fully presented, and the impact on the educational process when teachers use these technologies is limited. Aiming to cover this gap, we will try to map the field of IoT technologies in secondary education through the available published empirical studies. Our study focused on IoT in secondary education (students between 12 and 18), in which students have a relevant background in technology, computing, and programming.

To support the purpose of our research, this paper is structured as follows: Section 1 (Introduction) provides an overview of the Internet of Things (IoT), outlines the research aims and questions, and defines the concept of affordances. Section 2 (Method) presents the research methodology and strategy, along with the inclusion and exclusion criteria. The validity and reliability of the results are ensured through the implementation of the PRISMA methodology. Section 3 (Results) reports the main findings of the systematic review, focusing on the technological and educational affordances of learning environments with IoT, as well as the impact on learning and teaching. Section 4 (Discussion) analyzes how IoT enhances learning by promoting collaboration, personalization, and inquiry-based approaches through technological and pedagogical means. Section 5 (Implications and Limitations) highlights the significant potential of IoT in education while emphasizing the need for institutional safeguards regarding data protection and identifies the necessity for further research on possible negative effects and implementation challenges. Finally, Section 6 (Conclusions) summarizes the key insights of the review and proposes directions for future improvements, including well-designed teaching scenarios, teacher training, affordable tools, and the integration of IoT into the formal curriculum.

1.2. Research Questions

Our research presents in detail the updated findings of the last 10 years, and through this, we will highlight how the use of IoT technology plays a vital role in improving learning. Considering what we have mentioned above, as well as the review of international scientific activity on the relationship and interaction between secondary school students and the Internet of Things, we set out to investigate and present the technological and educational characteristics, as well as the impact of the integration of the Internet of Things in education.

To fulfil these objectives, we formulated three research questions:

(a)

What are the technological affordances of the learning environments with IoT?

(b)

What are the educational affordances of the learning environments with IoT?

(c)

What is the impact on learning when teachers utilize IoT devices?

This systematic review will provide insights into many stakeholders. Researchers will use the results to identify gaps in existing knowledge to guide future research. Policymakers could use the findings to assess the need to formulate policies and guidelines to improve educational programs. Finally, teachers could modify their teaching methodology and practices based on the most cutting-edge technological advances.

1.3. Affordances

The concept of affordance was first proposed by Gibson [14], who wanted to describe an environment’s actionable properties. The action properties are objective but are directly related to the user who will use them. Lee et al. (2014, in Sadeck 2022) [15], refer to affordances as “the features of an object that indicate which actions a user can take and signify how they can interact with the object”.

The capabilities of tools or technology objects are not always the same, even if they belong to the same category. A physical game usually does not provide the same capabilities as its digital counterpart. In addition, the same environment does not present the same possibilities of action to all its potential users; for example, a ladder provides a climbing affordance for an adult, but not for a baby. According to Norman [16], affordance is fundamentally a relationship between the properties of an object and the capabilities of an agent (user), which together determine how the object can be used.

Since affordances arise from this interaction, they are relational rather than purely objective or subjective. He introduced the concept of perceived affordances within the context of design and human–computer interaction (HCI). Students have the opportunity to personalize their learning experiences by selecting applications and modes of presentation that align with their individual preferences and learning pace. The portability and usability of the devices offer authenticity to the learning process, enabling real-time recording of experiments and bridging the gap between theory and practice [17].

The concept has been widely adopted in education to describe the learning opportunities provided by digital environments and technological tools, which offer specific affordances that can influence how we learn, what we learn, and how we interact with others [18]. However, these technological tools should be examined not based on their attributes, but on the affordances they provide for specific pedagogical purposes [15]. For example, simulations facilitate experiential learning, online discussions support dialogue and collaboration, and content creation applications (e.g., video editors, spreadsheets) enhance creativity.

Researchers have used affordance theory to study 3D Virtual Environments, online social networks, scaffolded social learning, blogs and learning, science learning, and literacy [19]. In contrast to existing systematic reviews [8,13], our article focuses on mapping the “technological” and “educational” affordances that arise from the integration of Internet of Things (IoT) devices and applications in secondary education, documenting how different types of IoT tools offer educational opportunities.

2. Method

The research method we used is the systematic review, which, according to Snyder [20], is considered the most accurate and appropriate approach for collecting scientific articles from multiple scientific sources. Our comprehensive and systematic search of various databases and sources ensured that all study-related articles were identified, reducing the possibility of bias. The standardized checklist ensured that all necessary information was consistently reported, making it easier for readers to understand the review’s methodology, results, and conclusions. At the same time, future researchers can replicate the study, enhancing the results’ reliability and validity [21]. To answer our research questions, we searched for scientific papers using four significant databases: Google Scholar, Scopus, Education Resources Information Center (ERIC), and the IEEE Xplore Digital Library. These databases were selected due to their contribution to the validity of the research and their representation of the fields of education, technology, and sciences, which are closely associated with the present study.

2.1. Search Strategy and Inclusion Criteria

The extracted results were limited to peer-reviewed publications, specifically journal articles and conference proceedings that adhered to a blind review process. All selected documents were published in English, and the retrieval process was based on Boolean search logic to ensure both breadth and precision. The search strategy employed three core conceptual components: (i) Internet of Things (IoT), (ii) education, and (iii) secondary education. Indicative search phrases included “Teaching IoT in K-12”, “Internet of Things” OR IoT, “Internet of Things in Education” OR “IoT in Education”, combined with Secondary OR “12 to 18” AND NOT (“higher” OR “childhood” OR “primary”).

This search syntax was designed to filter studies explicitly relevant to IoT-related educational interventions within the context of secondary education, excluding those focused on higher education, early childhood, or primary levels. Subsequently, in order to check the completeness of the results, a search of each journal’s website was executed using the same keywords.

Following the initial search, a predefined set of inclusion and exclusion criteria was applied to determine the eligibility of each article (see

Table 1. Selection criteria.

No.	Inclusion Criteria	Exclusion Criteria
1	IQ1: include empirical studies in peer-reviewed publications (conference or scientific journal)	EQ1: Exclude studies that are not peer-reviewed
2	IQ2: include empirical studies in English language	EQ1: exclude papers in any language other than English
3	IQ3: include Empirical studies utilizing IoT in secondary education	EQ1: exclude studies conducted in preschool, primary, further, or higher education
4	IQ4: include studies published between January 2013 and December 2024	EQ1: exclude publications published before 2013

). Studies were excluded if (i) they were published prior to 2013, (ii) they were not subject to peer review, (iii) they were not written in English, (iv) they lacked alignment with the defined search terms, or (v) they did not present empirical evidence relevant to IoT implementation in secondary education contexts.

The selection of 2013 as the starting point for this review was based on a trend analysis using Google Trends, which indicated a sharp global increase in interest in the term “Internet of Things” beginning in early 2013 and continuing through 2016. This upward trend reflects the emergence of IoT as a topic of growing relevance not only in literature but also in broader societal discussions. Prior to 2013, the term had minimal visibility both online and in scholarly publications, suggesting limited public awareness and academic engagement with the concept.

In light of this evidence, this review focuses on the period from 2013 to 2024 in order to trace the evolution and contemporary relevance of IoT within educational contexts. This timeframe allows for a comprehensive examination of how the technology has transitioned from an emerging concept to a widely discussed and implemented component of modern life and education.

Furthermore, studies that did not include the specific search terms defined in our methodology or lacked adequate empirical grounding were excluded. The pronounced spike in global searches for “Internet of Things” during this period reinforces its growing integration into everyday life and supports the rationale for our chosen timeframe of analysis.

The following methodology was followed to reduce the risk of rejection of tasks related to our research. In the first phase, based on the search terms, a total of 1914 studies emerged from the four databases, of which 1643 were rejected due to not meeting the established criteria or due to duplicate registrations between the databases. Of the 271 studies that remained, 20 of them could not be accessed or retrieved. In the next step, 226 studies were excluded that either did not refer to secondary education and ages between 12 to 18 or K-12 or referred to research with no application scope (empirical research). Finally, 25 studies (5 published in journals and 20 in conferences) were accepted for this study (Figure 1).

2.2. Categories of Analysis

From the final selected studies, the following information was extracted into an Excel sheet to help us organize and group the findings of our research: (1) title, (2) date, (3) authors, (4) purpose, (5) research questions, (6) methodology, (7) results, (8) tools used, (9) keywords, and (10) source of extraction.

According to the research questions, the two main categories of analysis proposed are (a) technological affordances and (b) educational affordances. Specifically, regarding technological affordances, the classification was guided by the nature of the technology (tangible interfaces, smart objects, and IoT applications), the mode of interaction, the presence of sensors, the level of connectivity, and the complexity of the ecosystem. Likewise, for educational affordances, the categorization was based on the structure of the activity (structured or open-ended), the roles of the teacher and students, and the degree of student autonomy and involvement in the design process. The application of these criteria enabled a consistent and well-substantiated classification of the studies into specific categories, thereby enhancing the methodological validity of the review.

2.2.1. Technological Affordances

Technological affordances refer to specific characteristics that distinguish them from traditional applications or other digital applications that do not present the so-called “smart features”.

Table 2. Features of tangible interfaces, smart objects, and IoT applications.

Feature	Tangible Interfaces (TIs)	Smart Objects (SOs)	IoT Applications (IoT Apps)
Definition	Physical objects that can be interacted with to provide input to a computer system	Objects embedded with sensors and computational capability	Network of physical devices connected via the internet to collect and exchange data
User interaction	Direct manipulation of physical objects	Interaction through sensors and embedded intelligence	Interaction through connected devices, often remotely
Main objective	Enhancing human–computer interaction through tangible means	Adding intelligence and interaction to everyday objects	Creating a connected ecosystem of devices to share and analyze data
Examples	Interactive surfaces, tangible user interfaces. e.g., “Educational game with RFID cubes that the student places on a screen table and watches visual reactions”.	Smart thermostats, lights, smartwatches, and RFID-enabled objects. e.g., “A smart pot that detects soil moisture and alerts the user when the plant needs watering”.	Smart homes, smart cities, smart agriculture, industrial IoT systems, and healthcare. e.g., “A smart farming system that collects data from soil, temperature, and light sensors, and automatically adjusts watering”.
Technology integration	Uses physical objects integrated with digital systems	Integrates sensors, actuators, and processing units into objects	Utilizes the internet, cloud computing, and data analytics
Application	Education, gaming, design, and creative arts	Personal gadgets, healthcare, and security	Home automation, industrial automation, transportation, and agriculture

groups, compares, and describes the three major categories of technological affordances as derived from the analysis: (a) tangible interfaces (TIs), (b) smart objects (SOs), and (c) IoT applications (IoT apps). The information gathered for each category pertains to (i) definition, (ii) mode of interaction with the user, (iii) primary purpose of use, (iv) example applications, (v) technological integration, and (vi) field of application.

The first category, (a) tangible interfaces (TIs), corresponds to tangible representations of information. Users can grasp data with their hands and influence functionality through the natural manipulation of these representations [22]. TIs are ideal for introducing concepts such as energy, electronics, or programming languages using physical objects.

For example, in a Technology or Informatics class, students might create an “alarm system” using physical blocks. Motion detection by a sensor triggers the activation of the alarm, aiming to help students understand logical sequencing as well as the concept of “if-then.” Logical thinking is cultivated in an experiential and playful manner.

The second category refers to (b) smart objects (SOs), which include devices and physical elements that can be recognized and interact with the environment and other objects. Moreover, they can act intelligently and independently under certain conditions, communicate with different objects, process environmental data, and complete “events” [23]. They observe the environment and correlate data with real-world phenomena. For example, in a Biology class, students use appropriate sensors to record watering frequency, temperature, and their impact on plant growth. This contributes to the development of environmental awareness and the understanding of biological variables through the application of scientific experimentation.

The last category of technological affordance concerns (c) IoT applications (IoT apps) defined as a network of physical devices used to collect and exchange data. It is ideal for use in interdisciplinary Science, Technology, Engineering, and Mathematics (STEM) programs and collaborative learning projects. For example, students design and program an ecosystem that contains smart objects equipped with sensors, networking, and processing technologies that integrate and operate together. By creating this ecosystem, such as a farm, a greenhouse, or an aquarium, in which intelligent services are implemented, students have the opportunity to understand the cycle of recording, analysis, and intervention [24].

Each of these categories has different affordances and technological infrastructures, offering complementary functions and significant synergies in educational practice. Physical objects (TI) can be augmented with sensors and actuators integrated within broader IoT systems, enabling physical objects to exhibit smart behavior by interacting with the environment and transmitting data. In this sense, the enriched tangible interfaces (TIs) can activate or manage IoT systems. Smart objects (SOs), in turn, collect data that is transmitted to a cloud platform for user-based processing, such as analysis, comparison, and decision making. In this context, the integration of sensors into physical objects and their connection to the internet leads to the full functional coupling of the three categories.

Beyond complementarity, there are also overlapping functions between them, particularly in the capabilities of, e.g., Arduino or MicroBit. Their specifications are geared toward learning programming, automation, and robotics, and they are widely used in the educational process. Their primary orientation is to function as core components of an IoT application, as they collect, process, and transmit data to the cloud. In addition, depending on how they are used and the context in which they are integrated, they can function as SOs, since they are equipped with a motion sensor or gyroscope.

In conclusion, the combined use of TI, SO, and IoT applications forms an integrated learning framework that is interactive and technologically enriched. Despite their differences, these categories operate complementarily and often overlap, especially through Arduino and MicroBit. Their integration enhances experiential learning; deepens understanding of technological systems; and supports the development of programming, analytical, and problem-solving skills.

2.2.2. Educational Affordances

Educational affordances pertain to the educational context, specifically the types of activities they enable and the learning objectives attained through the utilization of IoT technology. The corresponding codes for this category are described as follows: (a) organised educational activity (OEA), (b) design-based learning (DBL) activity, and (c) a combination of these two educational activities (Appendix A 

Table A1. Classification of activities and affordances.

No.	Approach	Year	Authors/Title of Paper	Technological Affordances 1	Device/Equipment	Educational Affordances		Age/Sample
						Activity 2	Subject—Learning Area
1.	Gamification	2017	Petrovic L. et al. [53] Development of an educational game based on IoT	TI-SO-IoT	Mobile app, software	OEA	Computer Science Smart Environment	Secondary, small group of students
2.	Educational activity	2018	Tziortzioti C. et al. [52] Observation and Analysis of Environmental Factors of Surface Waters: An Internet of Things Educational Approach	SO-IoT	DS18B20 Digital Temperature Sensor Dissolved Solids Meter Dissolved Oxygen Meter Turbidity Sensor pH Meter Arduino Device, Arduino platform	OEA-DBL	STEM, Physics—Chemistry	Secondary, 16–17 y.o. 30 students
3.	Workshop	2018	Mavroudi A. et al. [47] Designing IoT applications in lower secondary schools	TI	IoT inventor toolkit (Tiles design workshop, Tiles inventor materials (board, playbook, cards))	DBL	Brainstorming—Problem Solving	Secondary, 14–15 y.o. 17 students
4.	Ubiquitous-Mobile Computing	2018	Glaroudis D. et al. [56] Investigating Secondary Students’ Stance on IoT Driven Educational Activities	TI-SO-IoT	UMI-Sci-Ed educational platform IoT Udoo-Edu kit (Udoo Neo board, ultrasonic sensor)	OEA	Problem-Solving Activities, Critical Thinking	Secondary, 14–16 y.o. 63 students
5.	air:bit programmable sensor kit	2018	Fjukstad B. et al. [38] Low-Cost Programmable Air Quality Sensor Kits in Science Education	TI-SO-IoT	Air:bit kit, Arduino UNO, temperature and humidity sensor, optical dust sensor	OEA	Computer Programming—Engineering	Secondary, 26 students
6.	STEM Education in School	2018	Kusmin M. et al. [60] Smart Schoolhouse—Designing IoT Study Kits for Project-based Learning in STEM Subjects	TI-SO-IoT	Data analysis software, smart sensors, portable lab	DBL	STEM Education	Basic and Secondary, Large group of students
7.	IoT aquatic environment system	2019	Tziortzioti C. et al. [35] IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial	SO-IoT	Arduino (hardware, software), IoT sensor kit (temperature, dissolved solids, turbidity, dissolved oxygen, PH)	OEA-DBL	Mathematics, Physical Science	Secondary
8.	High School course	2019	Christine Julien [69] Using the Internet of Things to Teach Good Software Engineering Practice to High School Students	TI-SO-IoT	Android app, smart lights, Breadboard, Raspberry Pi, Bluetooth	OEA-DBL	Computer Programming	Secondary
9.	Summer School hands-on activities	2019	Glaroudis D. et al. [45] STEM Learning and Career Orientation via IoT Hands-on Activities in Secondary Education	IoT	UMI (ubiquitous, mobile computing and Internet of Things) platform UDOO kit	OEA	Programming, Technology, Math	Secondary, 14–16 y.o. 64 students
10.	GAIA lab kit	2019	Mylonas G. et al. [39] Enabling sustainability and energy awareness in schools based on iot and real-world data	TI-SO-IoT	Raspberry Pi, Conductive ink markers Electronic components, sensors Electronic boards Arduino-based IoT node	OEA	Energy Issues	Secondary, 12–16 y.o. 106 students
11.	Experimentation learning Training in the field	2020	Chen A. et al. [44] Schoolchildren’s exposure to PM2.5: a student club–based air quality monitoring campaign using low-cost sensors	SO-IoT	Wearable sensors, cloud server	OEA	Air pollution	Secondary, 8 students
12.	Hands-on teaching activities	2020	Spyropoulou N. et al. [55] Fostering Secondary Students’ STEM Career Awareness through IoT Hands-On Educational Activities: Experiences and Lessons Learned	IoT	Evaluation	OEA	STEM	Secondary, 14–16 y.o. 150 students
13.	School Workshop	2020	Schneider G. et al. [65] Teaching CT through Internet of Things in High School: Possibilities and Reflections	SO-IoT	Electronic components, sensors, Arduino Uno, IoT web platform	DBL	Computational Thinking	Secondary, technical school ICT and agriculture
14.	Summer School	2020	Jaklic A. [41] IoT as an Introduction to Computer Science and Engineering: A Case for NodeMCU in STEM-C Education	IoT	Electronic components, NodeMCU hardware and software platform, sensors	OEA	Computer, Pro Engineering Ed.	Secondary, Technical Secondary 80 students
15.	School Educational program	2020	Moreira F. et al. [37] Open IoT technologies in the classroom—a case study on the student’s perception	TI-SO-IoT	Arduino, Wi-Fi module (ESP8266), sensors, Breadboard	DBL	Natural Sciences	Secondary 24 students
16.	Short-term course	2020	Ota K. et al. [70] A Short-Term Course of STEAM Education through IoT Exercises for High School Students	SO-IoT	Arduino, IoT Device Gateway, Raspberry Pi, sensors	OEA-DBL	STEAM	Secondary—High School students
17.	Workshop/ co-designing	2021	Aki Tamashiro M [32] How do we teach Emerging Technologies in K-9 Education	TI	Designing, software	DBL	Storyboard Human–Computer Interaction	Secondary, 14–16 y.o.
18.	School Educational program	2021	Anastasi G.F. et al. [71] Teaching IoT in the Classroom and Remotely	SO-IoT	Google Classroom and Edmodo platforms, Arduino, Raspberry, sensors	OEA-DBL	Computer Science	Secondary
19.	Course	2022	Trilles S. et al. [43] Sucre4Stem: collaborative projects based on IoT devices for students in secondary and pre-university education	SO-IoT	SucreCore, Sucre4Stem, Actuators, sensors, software tool	OEA-DBL	STEM—Computer Science	Secondary
20.	Gamification	2020	Mylonas G. et al. [54] Using gamification and IoT-based educational tools toward energy savings-some experiences from two schools in Italy and Greece	TI-SO-IoT	IoT-based lab activities Node-RED plugin LabKit sensors GAIA IoT platform	OEA	Energy	Secondary, 100 students
21.	Experimentation learning	2023	Stojanovic D. et al. [9] Empowering learning process in secondary education using pervasive technologies	SO-IoT	Mobile app, Raspberry Pi, Arduino, various sensors, QR objects	OEA	Technology	Secondary, 17–19 y.o. 37 students
22.	Hands on activities	2023	Sum K. et al. [36] Microcontroller Based Platforms For STEM Education	SO-IoT	ESP32 microcontroller, light sensor, passive infrared sensor, Arduino Board	DBL	STEM	Secondary, 14–15 y.o. 124 students
23.	Teaching activity	2023	Tamashiro M. A. et al. [46] Teaching technical and societal aspects of IoT—A case study using the Orbit IoT Kit	TI-SO-IoT	Orbit IoT Kit, MicroBit, MakeCode, web application	OEA-DBL	Technology Education	Secondary, 12–13 y.o. 20 students
24.	Hands on activities	2024	Oprea M., Mocanu M. [31] IoT in education—Photovoltaic panel systems	IoT	Arduino, Solar Panel, current–voltage sensor, IoT platform	DBL	Energy, Physics	Secondary
25.	Gamification	2024	Ahmed N. et al. [42] Bridging IoT Education Through Activities: A Game-Oriented Approach with Real-time Data Visualization	TI-SO-IoT	ESP8266-based Wi-Fi module, sensors, Raspberry, cloud, interface	OEA-DBL	STEM	Secondary

¹ Technological affordances: tangible interfaces: (TIs), smart objects: (SOs), Internet of Things: (IoT). ² Educational affordances—Type of activity: Organised Educational Activity (OEA), design-based learning (DBL).

).

An organized educational activity (OEA) is defined by UNESCO in the International Standard Classification of Education “ISCED 2011” as an activity with planned explicit or implicit objectives, including the necessary procedures to facilitate the learning environment and the teaching method through which communication is organized. It is integrated in a direct and predetermined way. It is part of a more comprehensive framework of developmental activities organized by the teacher, who is involved in the children’s game to guide, reinforce, and deepen them. The main characteristics of OEAs are as follows: (a) there is a structured curriculum—the activities follow a defined curriculum with specific learning objectives and outcomes; (b) it is instructor-led—typically a teacher, professor, or facilitator guides the learning process; (c) there are assessment and evaluation—participants’ progress and understanding are often assessed through tests, assignments, projects, or other evaluation methods; (d) it is time-bound—they usually have a set duration, such as a semester, a workshop day, or an online course schedule; (e) there is interactive learning—many activities encourage interaction among participants and between participants and instructors, fostering a collaborative learning environment.

According to Felix [25], a DBL is learning that comes from evaluating understanding through design. This approach focuses on active hands-on learning, where students engage in real-world problem-solving and iterative design processes. Students take an active role in their learning, driving their projects based on their interests and questions, working in teams, and developing important skills in collaboration and communication. Teachers act as facilitators or mentors rather than traditional instructors, as in the previous category. Some key elements are that students face authentic problems, making learning more relevant and interesting. Issues are often open-ended, requiring creative solutions and emphasizing iterative cycles of designing, prototyping, testing, and improving solutions, encouraging a growth mindset where failure is seen as a step toward improvement. However, according to Komis et al. [26], the skills developed are related to specific content knowledge (mathematics, physics, computer science, etc.) or skill development. Usually, the learning objectives are related to the constructionist approach of developing high-level thinking skills, such as problem-solving, critical thinking, collaboration, communication, and creativity.

Finally, some empirical studies adopted a combined educational approach that integrates organized educational activities (OEA) with design-based learning (DBL), highlighting the complementary affordances of structure and creativity. This hybrid model enables both guided instruction and student autonomy, fostering active engagement, collaboration, and problem-solving. The synergy between these two educational strategies, particularly within STEM contexts, enhances both conceptual understanding and the development of transferable skills. When supported by IoT technologies, this dual approach creates authentic, meaningful, and student-centered learning experiences.

2.3. Data Coding

To ensure the reliability and consistency of the data analysis process, a two-stage coding procedure was implemented. The initial round of coding was independently conducted by the first author, followed by a re-coding of the same dataset by the second author. To assess the level of consistency between the two raters, Cohen’s Kappa (κ) statistics were calculated. The resulting inter-rater reliability was approximately 0.85, indicating substantial agreement according to the interpretive scale proposed by Landis and Koch [27], where values between 0.81 and 1.00 suggest almost perfect agreement. Discrepancies in coding were carefully reviewed through collaborative discussion between the two authors. This consensus-based resolution approach ensured that all final codes reflected shared interpretations, enhancing both the validity and credibility of the coding framework [28]. This methodological rigor contributes to the trustworthiness of the qualitative analysis and aligns with best practices in content analysis and thematic coding procedures [29].

2.4. Study Screening Procedure

The study screening process applied in the present systematic reviews followed the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), which provides a transparent and reliable framework for the selection of scientific sources [21]. The adopted methodological approach involved a multi-stage procedure designed to ensure the validity of findings and minimize systematic bias. An initial literature search was conducted in internationally recognized academic databases, using predefined keywords and logical operators (AND/OR), and search filters such as publication date, document type, and language were applied.

Moreover, the selection of studies was performed in two consecutive phases. Firstly, titles and abstracts were screened to remove obviously irrelevant studies. Secondly, two of the authors proceeded with text assessment to confirm relevance and methodological adequacy. The results of the screening process are presented above in a PRISMA flow diagram (Figure 1).

2.5. Quality Assessment

A structured data extraction framework was employed to systematically collect and organize all relevant information from the included studies in a standardized manner [21]. The extraction process encompassed a comprehensive range of variables, including demographic information, bibliographic details, date of surveys, participants, outcomes, main results, research design, methodology, key findings, and limitations [26]. In addition, the form captured the main findings, reported limitations, and, where available, indications of effect size or significance. This structured approach ensured consistency, minimized reviewer bias, and facilitated reliable cross-study comparison for narrative synthesis [30].

2.6. Systematic Review Registration

This systematic review was not registered in a publicly accessible protocol repository. While we followed the PRISMA guidelines for systematic reviews, no formal protocol registration was performed prior to conducting the review.

3. Results

3.1. Technological Affordances of the Learning Environments with IoT

The present unit describes the categorization of empirical studies regarding their technological affordances and/or their combination of them. As it emerges, not only from the state of the art but mostly from the data under study, it is evident that research interest covers a broad range of scientific fields, such as Science, Technology, Engineering, Arts, and Mathematics (STEAM), including computer science; robotics; the Internet; and specific areas of computing, such as user interfaces and interaction design. In Figure 2, the technological affordances of the learning environments with IoT are presented in ascending order based on their prevalence in the selected empirical studies.

Analyzing the results from the perspective of technological affordances, it appears that IoT systems were developed into the educational process in 23 of the 25 studies as the primary technological affordance. Meanwhile, in the two remaining cases [31,32], IoT was not utilized as a primary technological affordance. In nineteen of the studies, we noticed that smart objects were used to take measurements of various variables, such as air quality, turbidity, and water hardness, as well as energy consumption. Finally, twelve of them utilized tangible interfaces, primarily in the context of gamification or laboratory kits.

The use of both smart and non-smart IoT development devices fosters collaboration between teachers and students, as well as among students themselves, enhancing learning and communication. These devices promote teamwork in educational settings by enabling seamless information sharing [33]. For example, smart boards, tablets, and wearables that support the Internet of Things (IoT) allow students to collaborate and communicate in real-time. Students share resources, work together on tasks, and provide feedback to their peers using IoT-enabled devices. The overall use of technological affordance improves student and teacher engagement, while real-time communication is facilitated by platforms and apps, enabling students to seek clarification from instructors, ask questions, and receive feedback. This seamless communication facilitates the creation of a useful and engaging learning environment [34].

However, in addition to smart and non-smart objects, the equipment used in the empirical studies also includes handmade constructions [35]. A waterproof box serves as a protective enclosure for electronic devices, or a greenhouse, where students assemble various modules with Arduino, electronic boards, books, and cards, as well as microelectronic components such as resistors, LEDs, and motherboards [36,37]. A key prerequisite for supporting the hardware of experiments (smart devices, non-smart devices, and hand-made devices) is using the necessary software to collect, store, and analyze the data to draw useful conclusions. Some of the projects we studied utilize applications installed on mobile phones that are used both for data collection and for QR and barcode reading; in addition, other applications use Arduino dashboard platforms, as well as cloud software that serves to store the data and access it at any time and from any place [38,39,40,41,42,43].

3.2. Educational Affordances of the Learning Environments with IoT

The recorded educational approaches were mapped as organized educational activities (OEA), design-based learning (DBL), or a combination of both methods. Overall, in organized educational activities, IoT applications show the highest level of integration; on the other hand, the tangible interfaces are used less frequently across both educational methods. All organized activities adopt technological affordances (TI, SO, and IoT) either individually or in combination, while the majority of inclusions concern the IoT applications. For example, Chen et al. [44] used both smart objects (wearables) and IoT systems in which air pollution is considered; on the other hand, Glaroudis et al. [45] and Ahmed et al. [42] exclusively used IoT platforms and a gaming approach, respectively, to teach STEM applications. Tamashiro et al. [46], Moreira et al. [37], and Fjukstad et al. [38] utilized the three technological affordances (TI, SO, and IoT) to measure environmental variables for students in order to understand natural phenomena such as physics and chemistry. On the contrary, the number of empirical studies based on design-based learning educational activity is limited to seven. Of these, only the study by Oprea and Mocanu [31] exclusively used IoT applications in combination with DBL, while only two studies, by Mavroudi et al. [47] and Aki Tamashiro’s [32], exclusively used tangible interfaces in combination with DBL activities. Notably, the combination of the two educational affordances (OEA and DBL), which, according to Laksmi et al. [48], provides a multifaceted and effective learning approach by enhancing student engagement and critical thinking, was identified in eight research studies, most of which focused on STEM education. Figure 3 provides an overview of how the two educational affordances and their combination are reflected in empirical studies.

Figure 4 illustrates the content knowledge related to IoT education, measuring its frequency of occurrence. The most frequently occurring subjects are Computer Science and Programming (eight times) and STEM (seven times), indicating a strong emphasis on IoT educational activities on programming, algorithms, and scientific applications. Science subjects such as Physics and Chemistry appear five times, suggesting a significant yet not dominant presence. Energy is recorded three times, reflecting the growing relevance of sustainability and smart energy solutions within IoT contexts. Additionally, Mathematics, Technology, and Engineering are each mentioned three times. This may indicate that mathematics and engineering are also emphasized in IoT-based education, and that these aspects are explicitly defined as distinct learning goals rather than being implicitly embedded within broader ones. In particular, problem-solving with IoT and critical thinking may reflect the development of transversal skills that are integrated into various subjects rather than being treated as distinct areas of content knowledge in the reviewed studies.

3.3. The Impact on Learning and Teaching

Overall, the findings suggest that the integration of new technologies, such as the Internet of Things (IoT), into the educational process has a significant impact by offering new opportunities and challenges in learning [8,10,39]. Studies by F. Caballero et al. [6], Bogdanović et al. [49], Farhan et al. [50], and Ramlowat & Pattanayak [51] emphasize the significance of integrating IoT and mobile technologies in education, highlighting their role in enhancing students’ knowledge while fostering greater interest in the learning process. Table 3 summarizes the studies that reported a substantial impact on learning and teaching.

The impact of these technologies on learning can be assessed through two dimensions: (i) STEAM learning and cognitive enhancement and (ii) skills development (exploration, collaboration, and autonomy).

The first focuses on strengthening STEAM skills, significantly enhancing cognitive domains such as science, technology, engineering, arts, and mathematics. Through IoT, students can develop skills like computational thinking, design thinking, and data-driven thinking. These skills are critical for addressing 21st-century challenges and achieving subject content goals in disciplines such as physics, mathematics, programming, biology, arts, and languages. IoT bridges the physical and digital worlds, enabling students to explore and learn through hands-on interaction with real-world data. This approach fosters a deeper understanding of fundamental physical, chemical, and technological concepts while enhancing students’ ability to perceive, correlate, and interpret physical parameters through accessible and user-friendly technologies [31,38,44]. Exploring and understanding science becomes easier for students, whether they work individually or collaboratively [10,38,39]. Students not only enjoy using IoT devices but also find that these technologies simplify the learning process, making them feel smarter and more productive [9]. From a cognitive development perspective, IoT-enhanced learning experiences facilitate higher levels of knowledge retention by integrating experiential learning with abstract theoretical concepts. Students who interact with IoT tools report a higher sense of engagement and motivation, finding that these technologies make complex subjects more accessible and enjoyable.

The second dimension is based on exploratory and collaborative learning strategies, focusing on the development of higher-order thinking skills, such as problem-solving, collaboration, critical thinking, and computational thinking [26]. IoT creates interactive learning environments where students actively engage through real-world activities and projects. Students can design IoT prototypes to solve real-world problems, thereby developing their practical skills. Data from sensors and smart devices enable monitoring and analysis, allowing students to correlate, interpret, and evaluate changes and variables [28,52]. Students use real data related to the parameters they examine to explain phenomena and develop collaborative skills and critical thinking to explain phenomena and solve problems. The participatory process of designing games or activities that incorporate IoT technology motivates students’ interest in exploration [53]. Additionally, competence, ease of participation, enjoyment, and perceived usefulness contribute to changing attitudes and perceptions, enhancing their intention to engage in similar activities. Acting autonomously and taking responsibility for their learning, students are more likely to demonstrate self-motivation, critical thinking, and self-directed learning skills. They are no longer entirely dependent on teachers but actively participate in the learning process by enhancing their existing knowledge, setting goals, seeking resources, and evaluating their progress [46]. IoT’s interactive nature enables authentic learning experiences, where students are no longer passive recipients of knowledge but active creators. By engaging in real-world problem-solving, they develop resilience and autonomy in their learning journey. Furthermore, the process promotes self-regulated learning, allowing students to set goals, seek resources, and assess their progress independently, reducing dependency on direct teacher instruction.

Within the context of autonomous learning, two key themes seem to emerge: (a) the role of the teacher as a mentor and guide in the learning process is both multifaceted and crucial, particularly when integrated with technologies such as IoT and pedagogical approaches like design-based learning (DBL) and organized educational activities (OEA) [47]. In this context, the teacher transcends the traditional role of information transmitter, becoming an animator, guide, and facilitator of the learning experience. (b) The second theme is the collaborative groups among participating students, the importance of which is emphasized in studies within the context of the educational process, especially when technologies such as IoT and smart objects are used. Group activities are not merely a way to make lessons more interactive but also a means of developing essential skills [26].

Table 3. Effects of educational interventions with IoT.

Activity	Learning and Teaching
Educational game in a smart learning environment	Positive impact on students’ knowledge and attitudes. Students found the game useful and easy to use, which led to satisfying new knowledge acquisition. IoT knowledge test results were significantly better after using the game [53].
Quiz to measure student’s Active Experimentation (AE) abilities	The knowledge quiz provided to the learners the opportunity to test their ideas and knowledge by answering questions on IoT applications in everyday life problems [47].
Educational program using NodeMCU	Teachers gained new theoretical knowledge and practical solutions [41].
GAIA project using IoT hardware and software	Teachers noticed a “very significant change” in students’ performance and greater interest in programming. Low-performing students exhibited their capabilities [39].
Smart learning environment using IoT devices and mobile phones	A statistically significant difference in learning outcomes compared to standard tests. Students found the IoT devices useful, productive, and easy to learn [9].
Lessons incorporating an IoT light control switch	A total of 76 out of 124 students showed improvement on a Computer Literacy final examination [36].
Sucre4Stem implementation	Students took greater advantage of the free development part of each session because of the absence of technical problems [43].
Lab kit activities	Educators reported “positive changes in daily class activity and greater interest towards programming”. “Low-performing students had a chance to exhibit their capabilities and receive positive comments from the rest of their class” [54]
STEM program	Both Relative Learning Gain (RLG) and Absolute Learning Gain (ALG) averages were positive, which confirms that the educational scenario helped students understand basic concepts and gain new knowledge [55].
Summer school activities	Students commented that they gained new skills and knowledge in IoT applications. The experience was described as useful, exciting, constructive, excellent, fun, and interesting [41,45]
UMI-Sci-Ed project	Students changed their view of learning by realizing that the learning process could be interesting, funny, and pleasant [45,56]

Table 3. Effects of educational interventions with IoT.

Activity	Learning and Teaching
Educational game in a smart learning environment	Positive impact on students’ knowledge and attitudes. Students found the game useful and easy to use, which led to satisfying new knowledge acquisition. IoT knowledge test results were significantly better after using the game [53].
Quiz to measure student’s Active Experimentation (AE) abilities	The knowledge quiz provided to the learners the opportunity to test their ideas and knowledge by answering questions on IoT applications in everyday life problems [47].
Educational program using NodeMCU	Teachers gained new theoretical knowledge and practical solutions [41].
GAIA project using IoT hardware and software	Teachers noticed a “very significant change” in students’ performance and greater interest in programming. Low-performing students exhibited their capabilities [39].
Smart learning environment using IoT devices and mobile phones	A statistically significant difference in learning outcomes compared to standard tests. Students found the IoT devices useful, productive, and easy to learn [9].
Lessons incorporating an IoT light control switch	A total of 76 out of 124 students showed improvement on a Computer Literacy final examination [36].
Sucre4Stem implementation	Students took greater advantage of the free development part of each session because of the absence of technical problems [43].
Lab kit activities	Educators reported “positive changes in daily class activity and greater interest towards programming”. “Low-performing students had a chance to exhibit their capabilities and receive positive comments from the rest of their class” [54]
STEM program	Both Relative Learning Gain (RLG) and Absolute Learning Gain (ALG) averages were positive, which confirms that the educational scenario helped students understand basic concepts and gain new knowledge [55].
Summer school activities	Students commented that they gained new skills and knowledge in IoT applications. The experience was described as useful, exciting, constructive, excellent, fun, and interesting [41,45]
UMI-Sci-Ed project	Students changed their view of learning by realizing that the learning process could be interesting, funny, and pleasant [45,56]

4. Discussion

Through our systematic review, we aim to establish a foundation for understanding the impact and benefits of integrating IoT devices and applications in secondary education. This study investigates the interplay of educational and technological affordances, aiming to identify and leverage the potential of educational and technological tools and environments to enhance learning. The research explores how these affordances contribute to student cognitive, emotional, and social development. Specifically, it examines how technology can facilitate collaboration through platforms and learning communities, improve teaching by enabling educators to personalize instruction, and support individualized learning by adapting to students’ diverse needs, interests, and learning paces.

The analysis focuses on the technological affordances of devices, including sensors, controllers, platforms, and handmade constructions, as well as their synergistic combinations. Three key categories of technological affordances, identified through empirical studies, are presented: tangible interfaces, smart objects, and IoT applications. The diverse range of equipment employed across these studies—such as sensors, processors, handheld devices, software, and support platforms—illustrates the multifaceted nature of IoT-enhanced environments. For instance, research by Mylona et al. [54] on energy conservation in Greek and Italian schools leverages the affordances of tangible interfaces, smart objects, and IoT applications. Similarly, Tziortzioti et al.’s [52] study on the seawater environment utilizes bright objects and IoT platforms, including Arduino-based systems equipped with sensors for temperature, dissolved solids, turbidity, dissolved oxygen, and pH measurement.

These technological affordances—real-time feedback, interactivity, context-awareness, and data tracking—can be directly mapped to established learning methodologies. Inquiry-based learning is supported by the ability to capture and visualize environmental data, allowing students to observe, hypothesize, and test variables in real time. Project-based learning is enriched through the deployment of smart systems that enable long-term monitoring and iterative experimentation. Tangible interfaces foster constructivist learning by enabling hands-on manipulation and the exploration of cause–effect relationships. Additionally, the personalization and data persistence offered by IoT platforms support self-regulated learning by allowing learners to monitor their progress, set goals, and reflect on outcomes. Understanding these interconnections is crucial for designing pedagogically meaningful and methodologically aligned IoT-enhanced educational scenarios [57,58].

Furthermore, this study investigates educational affordances in relation to activity types, which are categorized as organized educational activities (OEA) and design-based learning (DBL). Notably, the studies by Tziortzioti [10,52] effectively integrated both OEA and DBL within the context of environmental observation and surface water analysis. This integration allowed students to actively participate in real-world scientific inquiries, applying theoretical knowledge to practical scenarios. Through this approach, students were able to explore and understand key concepts such as water quality, environmental factors, and the impact of human activities on natural resources, thereby deepening their comprehension of scientific principles and fostering critical thinking skills.

In this context, educational affordances play a central role in determining how IoT technologies translate into meaningful learning experiences [59,60]. These affordances refer to the perceived learning opportunities that emerge through interaction with IoT-enhanced environments—such as the promotion of collaboration, inquiry, and reflection. For instance, the integration of sensor-based experiments within structured or open-ended activities affords students the opportunity to formulate hypotheses and engage in scientific reasoning [61]. Real-time data visualization supports metacognitive processes [62], while learning tasks involving tangible interfaces foster peer dialogue and knowledge co-construction [63]. Thus, the synergy between the pedagogical structure of the activity (OEA or DBL), the learning environment, and the embedded educational affordances is essential for scaffolding effective instruction and optimizing learning outcomes.

Finally, this research identifies and thoroughly analyzes the learning objectives emerging from the studied activities, making a clear distinction between specific and general objectives [26]. Specific objectives refer to the acquisition of domain-specific knowledge, such as mathematical or scientific concepts, where students gain a deeper understanding of particular theories, formulas, or phenomena. In contrast, general objectives focus on broader, transferable skills, such as problem-solving, critical thinking, and collaboration, which are essential for students’ overall intellectual development and can be applied across various contexts and disciplines. This distinction allows for a comprehensive understanding of the educational outcomes associated with each activity type.

Through our systematic review highlighted the positive impact of IoT usage in secondary education. But how could we maximize its benefits? Appropriate educational scenarios focused on real-world problems that integrate IoT in a way that is relevant and engaging for students are required [1,14,33,34,36,38,39,40,52]. Scenarios that relate learning to the real world increase interest and provide opportunities for hands-on practice [5,9,34]. They need to be responsive to the diverse learning styles and interests of students. Teachers need to be trained in the use of IoT technologies, possessing the necessary skills to create appropriate learning experiences [1,31,44,45]. Collaborating with universities, building communities of practice, and sharing knowledge and experiences with other educational researchers and experts can help address this issue [47]. The tools and platforms developed and used should be affordable and user-friendly, allowing students and teachers to easily create, test, and manage IoT projects. Moreover, the use of open source and low-cost solutions such as Raspberry Pi, Arduino, and MicroBit can reduce the cost of implementing IoT in education [5,32,40,49]. Last but not least is the need to integrate IoT into the curriculum in a way that it is linked to existing courses and not a fragmented activity [1,11,39,50,51,53,64,65]. Regular evaluation and feedback on the effectiveness of this integration is essential for continuous improvement [16,36].

The use/utilization of IoT in secondary education is not only a technological development in the sector but can also change educational approaches and perceptions [4]. It reshapes education by shifting away from traditional teacher-centered practices, enhancing learning through real-time data collection and the analysis of natural phenomena. Learning is enhanced through hands-on applications, as students can interact with accurate data, which drives them to understand concepts through sensors and connected devices better, while at the same time improving the level of collaboration between students and between students and teachers.

5. Implications and Limitations

Implication for practice and policy: The use of technology as a teaching tool, along with its associated benefits, suggests a promising future and supports specific areas of learning and skill development. In this context, although educators strive to utilize emerging technologies and IoT in pedagogical processes, we found no evidence of integration into the formal education curriculum.

To maximize the positive impact on students’ knowledge, attitudes, and perceptions, schools, educational organizations, and policymakers should facilitate this integration through strategic educational planning. Another critical issue is student data privacy and ethical considerations. IoT devices collect and process vast amounts of data, raising concerns about security, consent, and responsible usage. To address these challenges, schools and educators must implement robust policies that safeguard student information while ensuring the ethical and responsible deployment of IoT technologies in the classroom. This includes clear data governance structures, transparency with stakeholders, and teacher preparedness to manage privacy issues in educational technology.

On the other hand, the potential financial challenges of integrating IoT into the educational process should also be considered. Educational institutions, particularly schools, have limited financial resources, and the cost of acquiring and integrating smart devices, sensors, and computing infrastructure can be prohibitive [64]. Furthermore, expenses related to transporting students to external sites for field exercises, experiments, and measurements, as well as obtaining permissions from school administrations and/or local authorities, should not be overlooked.

Implication for research: The findings of this review indicate that there is a strong body of research presenting the development and implementation of smart systems for educational purposes. Although we did not study the effectiveness of smart devices in cognitive development and learning, as the available research data did not always focus on such questions, further research in this direction is necessary. The empirical studies selected in this review neither explored nor managed to identify any potential negative impacts that IoT devices might have on students during their learning processes. This phenomenon might be attributed to publication bias, which refers to the tendency of researchers to publish only positive study results, as negative findings are more challenging to publish [66]. Nevertheless, as early adopters of new technology, children may be exposed to risks [67], making it urgent to investigate the negative aspects of IoT device use, such as technological dependency and distraction [68].

Furthermore, the use of IoT devices and the data they collect emphasizes the need for increased awareness and education on data security. However, knowledge and experience on how to effectively teach young children about data security in IoT devices remain limited.

Limitations: This study was limited to journal and conference articles published in English from four major databases. As a result, relevant publications outside this scope were not included in the review. Future research could benefit from expanding the search to additional databases and incorporating studies published in other languages. Moreover, similar analyses could be conducted using alternative sources, such as master’s or PhD theses, book chapters, or working papers. A key concern regarding IoT in education is cognitive overload—the complexity of IoT systems and the abundance of real-time data may overwhelm students, particularly those with limited technical backgrounds. Additionally, the interactive and immersive nature of IoT tools can sometimes become a source of distraction, diverting attention from core learning objectives.

6. Conclusions

This study examines the integration of the Internet of Things (IoT) in secondary education over the past decade, highlighting its technological and educational affordances and their impact on learning. Analyzing 25 empirical studies, we recorded and categorized the technologies used in tangible interfaces, smart objects, and IoT Applications. We found that IoT applications are the most widespread. Additionally, we classified the educational affordances based on the educational approach into organized educational activities (OEA) and design-based learning (DBL), noting a growing trend toward combining both approaches. The review revealed that IoT integration positively impacts learning by strengthening STEM skills through hands-on interaction with real-world data and fostering higher-order thinking skills like problem-solving and collaboration. The teacher’s role evolves into a mentor and guide, while collaborative learning among students is crucial. The study highlights the need for well-designed, real-world-focused learning scenarios, teacher training, affordable and user-friendly tools, and curriculum integration. While this review demonstrates the potential of IoT to transform education, it also identifies gaps in research, such as the need to explore potential negative impacts and address data security concerns. Future research should expand the scope of the review to include additional databases and publication types and further investigate the long-term effects of IoT integration on student learning and development.

Moreover, this study constitutes a significant contribution to the field of education, offering valuable insights and analyses that benefit a wide range of stakeholders, including educators, students, researchers, educational material designers, parents, and policymakers. For educators, it offers a systematic analysis and practical information on integrating IoT technologies to enhance learning, increase student engagement, and support skill development. Students benefit from easier access to IoT technologies, fostering an interactive and engaging learning environment that helps develop cognitive, social, and motor skills. Researchers receive a systematic review of existing literature, identifying knowledge gaps and areas for future research. Educational material designers can use the findings to develop innovative tools, understanding the potential and challenges associated with IoT technologies. Parents and policymakers gain information on the benefits and impact of IoT, aiding in informed decision making and policy development for safe and effective integration into education. Although this review draws primarily on empirical studies from specific educational systems, mainly in Europe, the synthesized findings and proposed recommendations aim to inform an international audience. The technological and educational affordances identified, along with suggested integration strategies for IoT in secondary education, reflect broader trends in 21st-century learning and can serve as a framework for implementation across diverse contexts. Nonetheless, when transferring these practices internationally, it is crucial to consider local curricula, policy constraints, infrastructural differences, and teacher readiness to ensure effective adaptation and impact.