Innovative Design of Internet of Things-Based Intelligent Teaching Tool with Application Using Quality Function Deployment ^†

Abstract

With globalization and technology advancement, traditional teaching models are facing challenges due to the diverse needs of modern learners. It is necessary to enhance learner engagement and motivation, and incorporating Internet of Things (IoT)-assisted teaching tools has become a major concern for educators. However, the time it takes to develop new teaching tools and integrate IoT technology must be shortened by combining educational content with game mechanics seamlessly. Therefore, we developed a gamified teaching model by incorporating IoT technology. We used the “System, Indicators, Criteria” framework to develop a three-tiered board game evaluation and development model. Based on this framework, a teaching tool was designed to provide personalized learning experiences with IoT technology. The tool provides abstract knowledge, fosters interaction and collaboration among learners, and thus enhances engagement. To ensure a rigorous design and evaluation process, we employed quality function deployment (QFD), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). The developed model facilitates the integration of IoT technology with innovative design concepts and enhances the application value of teaching tools in education. The model also enhances intelligence, interactivity, and creativity for traditional education to revitalize learning experiences.

1. Introduction

In education and training, educational gamification has been extensively used due to its potential to enhance students’ academic performance, engagement, and motivation [1]. Among these methods, board or tabletop games—valued for their tangibility—reduce cognitive risk and capture learners’ attention [2]. They are used to explain complex scientific concepts [3] and are useful for institutions lacking resources to develop computer-based games.

Technological advancements, including the Internet of Things (IoT), industrial IoT (IIoT), and Artificial Intelligence of Things (AIoT), have transformed education by enabling interactive, adaptive gamified learning experiences [4]. AI-driven tools enable personalized learning, while IoT sensors capture behavioral data during gameplay [5]. AI algorithms adjust game content and difficulty in real time, fostering engaging and collaborative learning environments. Despite the benefits, tools for systematically developing educational games are limited [6].

We integrated AIoT into gamified educational tabletop games. A three-tier evaluation framework—“System, Indicators, Criteria”—was introduced to enhance educational value and applicability. Quality function deployment (QFD), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE) were used to create a systematic, dynamic approach to transforming traditional education.

2. Literature Review

2.1. Gamification and Learning Motivation

Gamification has been applied to redesign various human activities, supporting overall value creation [7]. Its potential to boost learning motivation has been widely researched [8,9]. Motivation is categorized as extrinsic (driven by external factors) and intrinsic (driven by internal processes) [10]. In education, intrinsic motivation is related to learners’ engagement with material, aiding abstract concept understanding and higher-order thinking, which in turn promotes sustained effort and guided practice [11].

2.2. Board Game Design and Evaluation

Balancing subject matter and gameplay requires a clear design strategy, which is essential for successful gamification [12,13,14]. However, most academic research emphasizes design principles, while systematic processes that integrate game elements with educational content remain limited.

In practice, the Mechanics–Dynamics–Aesthetics (MDA) framework is widely used in game development. It breaks down games into rules, systems, and fun, aligning with mechanics, dynamics, and aesthetics from a design perspective [15]. Numerous design and evaluation principles have emerged based on this model. Following Almeida’s approach [16], we adopted a three-tier structure comprising systems, indicators, and criteria (

Table 1. Design criteria of educational board game.

System	Indicators and Criteria
Mechanics (E1)	To allow learners to assess and reinforce achievements with rewards (E1-1) [17,18]; Play literacy (E1-2) [17]; Challenge that matches player’s skill level (E1-3) [19,20]; To allow learners to have a sense of control over their success and develop learner confidence (E1-4) [18,19,21,22]; Adaptive Storyline and Context Based on Player Behavior and Decision-Making (E1-5) [5,7]; Balancing Cooperation and Competition to Enhance Engagement and Teamwork (E1-6) [6,7,8]; Hands-On Practice in a Simulated Environment (E1-7) [5,6]
Dynamics (E2)	To capture the interest of learners and stimulate their curiosity to learn (E2-1) [21,23]; Naturalisation (E2-2) [24]; Collecting data on player behavior during gameplay (E2-3) [5,7]; Real-time Monitoring and Feedback (E2-4) [5,6,7]
Aesthetics (E3)	Attraction and Preference (E3-1) [18,25]; Relevance and Embedding: To provide information that allows connection with learner’s prior experience; (E3-2) [21]; Task congruence (E3-3) [23,26]; Integration of Physical and Virtual Elements (E3-4) [5,6,7]; Contextual Immersion Experience (E3-5) [5,8]

).

3. Methods

We designed a framework to develop IoT-based educational board games. We collaborated with product designers and experts in education. In the first stage, QFD [27] and AHP [27,28] were applied to develop the concepts of the educational board game. To assess the feasibility of the design concepts, the FCE method was employed [27]. In the second stage, QFD was used to integrate IoT technologies into the game concept for the development of the game devices and the gameplay mechanisms.

• The design model introduced an innovative game mechanism that translated students’ play needs into feasible design features, with QFD as the core framework. The design requirements on the left wall of the House of Quality (Figure 1) were derived from the following literature review results: (1) Feedback: reward-based achievement reinforcement (E1-1); (2) Usability: play literacy (E1-2); (3) Control: appropriate challenge and learner autonomy (E1-3); (4) Interactivity: stimulating curiosity and interest (E2-1); (5) Naturalization (E2-2); (6) Relevance: linking new content to prior experience (E3-1); (7) Task Congruence: alignment between task and learning goals (E3-2).
• The importance weights of the seven design requirements were calculated by the design team using AHP. The process included the following steps: (1) Converting expert responses into a pairwise comparison matrix (Table 2); (2) Calculating weights via normalization of the geometric mean of rows (results in Table 3); (3) Conducting a consistency test to verify the validity of the questionnaire. Equations (1)–(3) present the AHP calculation formulas.

Table 2. Pairwise comparison matrix.

	Feedback	Usability	Control	Interactivity	Naturalization	Relevance	Task Congruence
Feedback	1	2	2	1	4	1/6	3
Usability	1/2	1	1/2	1/3	7	1/4	1/2
Control	1/2	2	1	1/3	4	1/3	2
Interactivity	1	3	3	1	7	1	4
Naturalization	1/4	1/7	1/4	1/7	1	1/4	1/3
Relevance	6	4	3	1	4	1	3
Task Congruence	1/3	2	1/2	1/4	3	1/3	1

Table 2. Pairwise comparison matrix.

	Feedback	Usability	Control	Interactivity	Naturalization	Relevance	Task Congruence
Feedback	1	2	2	1	4	1/6	3
Usability	1/2	1	1/2	1/3	7	1/4	1/2
Control	1/2	2	1	1/3	4	1/3	2
Interactivity	1	3	3	1	7	1	4
Naturalization	1/4	1/7	1/4	1/7	1	1/4	1/3
Relevance	6	4	3	1	4	1	3
Task Congruence	1/3	2	1/2	1/4	3	1/3	1

Table 3. Consistency test results.

λmax	RI	CI	CR
7.72	1.32	0.12	0.09

Table 3. Consistency test results.

λmax	RI	CI	CR
7.72	1.32	0.12	0.09

$$\mathrm{A}={\left[a_{ij}\right]}_{n\times n}=\left[\begin{array}{cccc}1& a_{12}& \dots & a_{1n}\\ {1/a}_{12}& 1& \dots & a_{2n}\\ ⋮& ⋮& 1& ⋮\\ 1/a_{1n}& {1/a}_{2n}& \dots & 1\end{array}\right]$$

(1)

$$W_i={\displaystyle \frac{{\left(\prod _{j=1}^n{a}_{ij}\right)}^{\frac{1}{n}}}{\sum _{i=1}^n{\left(\prod _{j=1}^n{a}_{ij}\right)}^{\frac{1}{n}}}},i=\mathrm{1,2},\cdots ,n$$

(2)

$$\mathrm{C}.\mathrm{R}.={\displaystyle \frac{C.I.}{R.I.}},\mathrm{C}.\mathrm{I}.={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(3)

CR was 0.09, indicating that the questionnaire was valid for the analysis. The results are summarized in Table 3.

For the technical features on the roof of the quality house, 22 feasible game mechanisms were selected from Boardgamegeek.com (Table 4), and 13 innovative ones were chosen by the design team based on thematic alignment. After calculating weights in the quality house model, the top seven mechanisms were used for development. The resulting board game helped students become familiar with key professional terms which are common industrial design terminology. Cards represented design concepts and terms, while components and coins tracked scores. During gameplay, students learned and reinforced professional knowledge in an engaging, interactive way.

Table 4. Weight of design requirements.

	Feedback	Usability	Control	Interactivity	Naturalisation	Relevance	Task Congruence
Importance Weighting	0.15	0.08	0.11	0.25	0.03	0.30	0.08
Weight Ranking	3	4	5	2	6	1	4

Table 4. Weight of design requirements.

	Feedback	Usability	Control	Interactivity	Naturalisation	Relevance	Task Congruence
Importance Weighting	0.15	0.08	0.11	0.25	0.03	0.30	0.08
Weight Ranking	3	4	5	2	6	1	4

4. Results

4.1. Expert Evaluation

In total, 31 experts participated in the evaluation: 18 male and 13 female, with 22 aged 21–30 and 9 aged 31–40 years old. The group consisted of 22 design experts, 4 from education, and 5 from other areas. Regarding professional experience, 6 had 5 years, 19 had 6–10 years, and 6 had 11–15 years. The expert questionnaire survey was conducted to determine the importance weights of evaluation indicators across three levels. At the system level, the weights were E1 (0.46), E2 (0.21), and E3 (0.33). At the indicator level, E1 was subdivided into E11 (0.30), E12 (0.18), E13 (0.13), and E14 (0.40); E2 into E21 (0.67) and E22 (0.33); and E3 into E31 (0.25), E32 (0.50), and E33 (0.25). At the most detailed criteria level, each indicator is further divided into specific items. For example, E11 consisted of E111 and E112, each with a weight of 0.50. In E12, E121 was weighted 0.67 and E122 was weighted 0.33. E13 included E131 (0.17), E132 (0.42), and E133 (0.42). E14 comprised E141 (0.46), E142 (0.17), and E143 (0.38). Within the E2 system, E21 included E211 (0.33), E212 (0.46), and E213 (0.21), while E22 consisted of E221 and E222, each assigned a weight of 0.50. As for the E3 system, E31 included E311 (0.29), E312 (0.42), and E313 (0.29); E32 consisted of E321 (0.67) and E322 (0.33); and E33 contains only one item, E331, with a full weight of 1.00, making it the sole and complete representative of its indicator.

After expert evaluation, factor evaluation matrices were summarized in

Table 5. Factor matrix.

	Very Bad	Bad	Average	Good	Very Good
E111	0	0.065	0.032	0.484	0.419
E112	0	0	0.065	0.613	0.323
E121	0	0	0.355	0.548	0.097
E122	0	0.065	0.29	0.516	0.129
E131	0	0.065	0.355	0.516	0.065
E132	0	0.033	0.167	0.567	0.233
E133	0	0.032	0.032	0.581	0.355
E141	0	0.097	0.129	0.645	0.129
E142	0	0.032	0.097	0.581	0.29
E143	0	0	0.129	0.613	0.258
E211	0	0	0.032	0.516	0.452
E212	0	0	0.097	0.355	0.548
E213	0	0.032	0.032	0.484	0.452
E221	0	0.032	0.097	0.516	0.355
E222	0.032	0	0	0.742	0.226
E311	0	0.032	0.161	0.516	0.29
E312	0	0.032	0.065	0.774	0.129
E313	0	0.032	0.194	0.548	0.226
E321	0	0.065	0.097	0.484	0.355
E322	0	0	0.065	0.677	0.258
E331	0	0	0.129	0.581	0.29

. The fuzzy comprehensive evaluation method (Equation (4)) was applied to calculate the results (

Table 6. Evaluation of results at all levels.

Levels	NO.	Very Bad	Bad	Average	Good	Very Good
Indicators	E11	0.00	0.03	0.05	0.55	0.37
	E12	0.00	0.02	0.33	0.54	0.11
	E13	0.00	0.04	0.14	0.56	0.26
	E14	0.00	0.05	0.12	0.62	0.20
	E21	0.00	0.01	0.06	0.44	0.50
	E22	0.02	0.02	0.05	0.63	0.29
	E31	0.00	0.03	0.13	0.63	0.20
	E32	0.00	0.04	0.09	0.55	0.32
	E33	0.00	0.00	0.13	0.58	0.29
System	E1	0.00	0.04	0.14	0.58	0.24
	E2	0.01	0.01	0.06	0.50	0.43
	E3	0.00	0.03	0.11	0.58	0.28
Overall evaluation		0.00	0.03	0.11	0.56	0.30

). The overall evaluation of the educational board game was as follows: 30% “Very Good,” 56% “Good,” 11% “Average,” 3% “Bad,” and 0% “Very Bad.” Based on the maximum degree of membership, the design was rated as “Good.” Criteria E1, E2, E3, E11, E12, E13, E14, E22, E31, E32, and E33 were rated “Good,” while E21 was rated “Very Good.”

$$\begin{gathered} \tilde{B}=\tilde{\mathrm{W}}·\tilde{\mathrm{R}}=\left(w_1,w_2,\dots ,w_n\right)·\left(\begin{array}{ccc}{x}_{11}& \dots & x_{1m}\\ ⋮& \ddots & ⋮\\ x_{n1}& \dots & x_{nm}\end{array}\right)=\left(b_1,b_2,\dots ,b_m\right), \\ and b_k=min\left\{1,\sum _{i=1}^n{w}_i{x}_{ik}\right\}\left(k=\mathrm{1,2},\cdots ,m\right) \end{gathered}$$

(4)

4.2. IoT-Integrated Board Game Design

The feasibility of the design in this study was evaluated by experts. Subsequently, the design team applied the QFD method to guide the development of the IoT-integrated board game and optimize the gameplay process. The left wall of the House of Quality (Figure 2) represents the design requirements, which were synthesized from the relevant literature. These requirements include connectivity, intelligence, analytics, interactivity, adaptability, security, and scalability. The importance weights of each design requirement were determined through in-depth discussions within the design team. An IoT-enabled board game was developed to enhance instructional effectiveness, with the design outcome and gameplay approach as illustrated in Figure 2.

The instructional board game integrated IoT technologies to help students learn industrial design terminology and enhance problem-solving skills. Key features included the following: (1) problem card challenges, where students choose components to address design scenarios; (2) AR interaction, enabling 3d model viewing and gesture control using AR glasses; (3) learning reports, generated by the ar system to track progress; and (4) expandability, through a universal card system for future content and customization.

5. Conclusions and Recommendations

Board games have gained popularity as recreational and “educational entertainment” tools. However, the development of traditional educational board games often lacks a well-designed framework linking instructional content with gameplay mechanics. Integrating IoT technology in education enhances learning effectiveness and expands delivery methods. We proposed an “Educational Board Game Design Model,” based on game motivation theories, using AHP and QFD to align educational goals with game design. The developed model in this study holds practical value for design applications. It is necessary to prototype the model to assess its usability and effectiveness.