Transforming Learning Environments: Asset Management, Social Innovation and Design Thinking for Educational Facilities 5.0

Abstract

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0.

1. Introduction

Educational Institutions are increasingly confronted with the challenge of addressing diverse learning styles and vocational aspirations among students (Antelm-Lanzat et al., 2020; Vorhaus, 2010). Traditional educational methods—such as auditory, abstract (intuitive), deductive, passive, and sequential approaches—have proven ineffective for many learners (Felder, 2002).

The Financial Crisis affecting educational institutions worldwide has been exacerbated by several elements, including the COVID-19 pandemic, demographic shifts, and inflation (OECD, 2021). However, these external factors may have merely highlighted the intrinsic need for innovative pedagogical approaches and models (Alkhnbashi et al., 2024; Green et al., 2020; Joseph & Ndeskoi, 2023; Reimers et al., 2022).

Students achieve better outcomes when educational strategies are tailored to their individual learning preferences and career aspirations (Bernacki et al., 2021; Odum et al., 2021). To effectively engage learners and address their diverse needs, educational practices must evolve to incorporate differentiated instruction and varied pedagogical approaches that promote active participation and support multiple pathways (Hu, 2024; Marks et al., 2021).

To facilitate this evolution in teaching practices, educational systems increasingly turn to well-established pedagogical frameworks such as constructivism, experiential learning, project-based learning (PBL), and 21st century skills development. These theories advocate for active, student-centered approaches that go beyond traditional content delivery to cultivate critical thinking, collaboration, creativity, and problem-solving abilities. Constructivist models emphasize knowledge construction through social interaction and meaningful engagement with the environment (Phillips, 1995), while experiential learning focuses on learning through reflection on doing (Kolb, 2014). PBL (Dimitra et al., 2016) and 21st century skills (Trilling & Fadel, 2009) frameworks further encourage authentic, interdisciplinary exploration of real-world challenges to prepare learners for the complexity of modern life. Together, these approaches call for learning environments and methodologies that not only support curriculum delivery but also foster deeper, more transformative educational experiences aligned with the demands of contemporary society.

Accordingly, learning environments must be designed as flexible, adaptive spaces that foster collaboration, creativity, and experiential learning. Such environments must enable educators to implement innovative, multidisciplinary teaching methods and address the diverse needs of the students (OECD, 2013).

In this context, researchers are exploring ways to innovate traditional classrooms (Papaioannou et al., 2023; Sasson & Yehuda, 2023), with some modifying these spaces into laboratories. This includes concepts such as learning factories, which simulate industrial environments and allow students to engage in hands-on, project-based learning that mirrors real-world processes (Abele et al., 2024; Barbieri et al., 2020; Rojas & Barbieri, 2019). Their use is particularly beneficial in technical vocational schools, where they support the development of various competencies, including multidisciplinary digital competencies (Roll & Ifenthaler, 2021).

Living labs, on the other hand, provide interactive environments where students can collaborate with community stakeholders to address real-life challenges, thereby enhancing their problem-solving skills and practical knowledge (Hossain et al., 2019). For instance, Favaloro et al. (Favaloro et al., 2019) propose the utilization of a campus as a living lab for student experiential learning in environmental sustainability.

Additionally, makerspaces are emerging as another type of innovative laboratory, equipped with tools and resources that encourage creativity and hands-on learning through design and fabrication activities (Mersand, 2021). In this regard, a systematic literature review highlighted that makerspaces effectively foster creative thinking and problem-solving skills among students, contributing to enhanced learning outcomes (Soomro et al., 2023).

While learning factories, living labs, and makerspaces provide valuable hands-on experiences, aligning these spaces with curricular goals, learning strategies, management practices, and the needs of the entire educational community is crucial for their success. However, to the best of the authors’ knowledge, there is no consolidated approach that achieves this alignment, which is essential for innovating educational institutions to effectively address the challenges they face.

In Design Thinking, the use of analogies serves as a powerful tool for uncovering new possibilities (Liedtka & Ogilvie, 2019). By drawing parallels to similar contexts, the following question can be addressed: “Where else do these conditions occur, and what solutions have been implemented in those scenarios?” This approach not only broadens perspectives but also facilitates the identification of innovative strategies that may be applicable to the current challenge.

In the context of the COVID-19 pandemic, a compelling analogy can be drawn between the crises faced by educational institutions and those encountered by industries. Both sectors experienced significant disruptions that forced them to reevaluate and adapt their operational models in response to unprecedented challenges. Just as industries were compelled to innovate their processes and embrace digital transformation to maintain productivity, educational institutions similarly faced the urgent need to shift to remote learning and develop new pedagogical strategies to engage students effectively.

In response to this situation, the European Commission introduced the concept of Industry 5.0 as a forward-looking framework to guide the transformation of industrial systems (Cotta et al., 2021). Industry 5.0 provides a vision that goes beyond efficiency and productivity as the sole objectives and emphasizes the contribution of industry to broader societal goals. It is defined by three core dimensions: human-centricity, which prioritizes the well-being and empowerment of people; resilience, which enhances the system’s capacity to respond to disruptions; and sustainability, which ensures the responsible use of resources and long-term environmental balance. This paradigm shift highlights the synergy between human creativity and technological advancement in shaping future-ready systems. Following this analogy, a pertinent question arises: ”What if the response to the challenges faced by educational institutions lies in their adoption of the principles of Industry 5.0?”

Few recent works in the literature address Education 5.0 (Ahmad et al., 2023; Shahidi Hamedani et al., 2024; Singh & Singh, 2024). This term aims to integrate advanced technologies into the education system to enhance the learning experience and eliminate barriers to individual education (Lantada, 2020). One of the fundamental goals of Education 5.0 is to promote personalized learning and collaboration through the use of digital tools such as artificial intelligence, virtual reality, and the Internet of Things, among others (Supriya et al., 2024). While these studies are valuable for investigating how modern technologies can enhance pedagogical methodologies, our approach focuses on the resilient, human-centric, and sustainable dimensions of Industry 5.0 to innovatively transform educational institutions in a holistic manner that encompasses learning environments, strategies, community needs, and management practices.

When addressing complex societal challenges—such as integrating Industry 5.0 dimensions into education—transdisciplinary approaches are essential (Bernstein, 2015; Nicolescu, 2014). These approaches are characterized by the following (Wickson et al., 2006): (i) a strong focus on real-world societal problems; (ii) the dissolution of disciplinary boundaries through the development of context-sensitive and evolving methodologies; (iii) collaboration among diverse stakeholders—including academics, practitioners, and the intended beneficiaries or ”clients” of the research. Rather than operating within a single academic framework, transdisciplinarity enables the integration of varied forms of knowledge to co-create meaningful and actionable solutions. This approach supports innovation and enhances the adaptability of educational practices to respond to emerging societal needs.

Building upon these foundations, this article proposes a transdisciplinary methodology that both integrates diverse academic disciplines and actively involves the educational community as key stakeholders. The approach brings together Asset Management (AM), Social Innovation (SI), and Design Thinking (DT) for the co-design of Educational Facilities 5.0—that is, educational environments that are resilient, human-centric, and sustainable. While the term educational facility primarily refers to physical assets such as classrooms, laboratories, and libraries, this article broadens the definition to also encompass pedagogical and management practices, as well as the educational community.

Given the novelty of the approaches and tools employed, and in line with DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this work also explores the role of SI and DT in AM, the influence of Large Language Models on convergent processes, and a vision for Educational Facilities 5.0. The article is structured as follows: Section 2 summarizes the main tools utilized in the methodology, while Section 3 presents the proposed transdisciplinary methodology. Section 4 applies the methodology to a case study for validation. Obtained results are discussed in Section 5 and finally, Section 6 presents the conclusions and sets the directions for future work.

2. Conceptual Background

This section provides a summary of the primary tools utilized in the transdisciplinary methodology. AM Value Frameworks are explained in Section 2.1, while a brief introduction to SI and DT are, respectively, illustrated in Section 2.2 and Section 2.3.

2.1. Asset Management Value Frameworks

AM is defined as the coordinated activity of an organization aimed at realizing value from its assets (ISO, 2024). In AM, value is obtained by acquiring assets that allow an organization to fulfill its strategic objectives (El-Akruti et al., 2013), and ensuring that the assets keep fulfilling those objectives throughout their lifecycle. Furthermore, value is achieved through the optimization of cost, risk, and performance (Barbieri et al., 2025).

In AM, a Value Framework (VF) defines the organization value elements (Roda et al., 2022), i.e., the elements deserve proper control because they are influential on value realization (Crespo et al., 2020). VFs are already employed across multiple industrial sectors to establish a comprehensive and standardized reference for prioritizing investment options and monitoring performance levels. These frameworks have found application in utilities, highways, and other asset-intensive sectors. For instance, the UIC (International Union of Railways) has defined a VF for railway Infrastructure Management organizations (UIC, 2020).

The structure of a Value Framework consists of distinct value dimensions, each capturing different facets of value (Almeida et al., 2022; Barbieri et al., 2024). Each dimension encompasses various value elements, represented as tuples of value drivers and value metrics. In this context, value drivers are the factors significantly influencing the realization of value, while value metrics are the performance measures used to assess these value drivers (Crespo et al., 2020). In this article, the VF is utilized to frame the definition of Educational Facilities within the Industry 5.0 dimensions.

2.2. Social Innovation

SI is based on the ability of individuals and communities to design new forms of collective action, creating social value through collaboration and active participation (Manzini, 2015). SI is generally characterized by four main stages: knowledge, co-creation, empowerment, and scalability. During the knowledge stage, strengths and weaknesses are understood, and a close, empathetic connection is established with individuals and the community, serving as a prerequisite for the success of the subsequent stages. Co-creation involves the collective construction of solutions by leveraging the community’s skills and interests to develop innovative solutions. In the empowerment stage, the community recognizes their insights in the solution, committing to its realization, as well as to the operation and maintenance of the future deployed asset or asset system. Finally, due to empowerment, the community becomes the main sponsor of the initiative, enabling the original project to be replicated in different settings, expanding in terms of coverage, impact, and cost-efficiency, thus achieving scalability of the initiative.

The application of SI in education is defined as Educational Social Innovation (ESI). ESI is framed within the epistemic of social constructionism and serves as a tool for conceptualizing, developing, and piloting educational innovations. This process involves a collective construction (co-creation) where all participants collaborate to provide effective solutions for identified contextual problems and opportunities (Roa De La Torre et al., 2021). Two primary goals of ESI are the communitarization and the development of the agency capacity of the educational community. ESI contributes positively to promoting at least four formative ideals of the emerging knowledge society: innovation, sustainability, entrepreneurship, and global citizenship (OECD, 2009, 2016; Roa De La Torre, 2017; Roa De La Torre et al., 2021; UNESCO, 2014, 2015, 2017).

For all the aforementioned benefits, SI is employed in this work. Furthermore, the co-creation and co-design empower the educational community, contributing to the long-term viability of the facility’s operations and enabling the scalability of the developed pedagogical and management model to other educational institutions.

2.3. Design Thinking

DT is a practical methodological process that has expanded the field of design beyond the physical and aesthetic, integrating both scientific research outputs and creative outputs to address complex problems (Buchanan, 1992). This inter-disciplinary approach combines divergent thinking, which fosters creativity and idea generation, with convergent thinking, focused on analysis and information synthesis (Cross, 1982; Lawson, 2006). The articulation of these thinking forms leads to innovative social solutions in products, processes, and services (Manzini, 2015).

This methodological approach investigates, explores, and gathers uncertainties, relationship patterns, and behaviours of human groups or communities Koskinen et al. (2013); Martin (2009). Brown (Brown & Katz, 2011) summarizes DT as a process of (i) inspiration, related to empathy and understanding of actors and/or the community; (ii) ideation, understood as creative and iterative processes of participation, co-creation, co-design, visualization, and prototyping in interdisciplinary teams; and (iii) implementation, as the alignment of strategic concepts and capabilities with the future reality.

DT has proven especially valuable in the development of learning environments and SI initiatives, as it fosters participatory design, community ownership, and creative problem-solving. In education, DT has been shown to enhance learner engagement and support the creation of flexible, student-centered spaces that reflect pedagogical innovation (Goldman et al., 2009; Henriksen et al., 2020; Razzouk & Shute, 2012). In SI contexts, it helps align diverse stakeholder values, and catalyze transformative change through iterative collaboration and empathy-driven exploration (Manzini, 2015). Given these benefits, DT is employed in this work during the inspiration phase to (i) address the complex, multi-actor challenges of educational facility design and (ii) facilitate the SI-based definition of Educational Facilities 5.0 through dialogue with the community.

3. Transdisciplinary Methodology for Educational Facilities 5.0

According to Systems Engineering (INCOSE, 2023), the creation of an asset/asset system consists of its definition, development, and implementation to meet customer requirements with the best possible outcomes. The methodology proposed in this article applies to the definition phase (or inspiration in DT) of Educational Facilities 5.0, while the development and implementation phases are left as future work. Additionally, while value metrics are integral to VFs, this article primarily concentrates on defining value dimensions and value drivers, with the specification of value metrics left for future exploration.

In this work, the SI stages presented in Section 2.2 have been adopted by integrating DT and AM principles and tools. The obtained transdisciplinary methodology for co-designing Educational Facilities 5.0 is illustrated in Figure 1 using the IDEF0 representation. IDEF0 facilitates the visualization of functions, detailing inputs, outputs, controls, and resources (mechanisms). In this context, ‘functions’ represent the actions involved in converting inputs into outputs, ‘resources’ (mechanisms) define the methods used for the conversion, while ‘controls’ signify the necessary alignment that must exist with the specified control elements throughout the process. Apart from the utilized DT tools, the image outlines the divergent thinking phases, which foster creativity and idea generation, and the convergent thinking phases, focused on analysis and information synthesis.

The methodology consists of the following steps:

• Define Focus Areas: In this phase, mutual understanding and reciprocal knowledge are established between the key stakeholders and the process facilitators before the subsequent brainstorming session. This phase involves activities that help understand each other’s perspectives, backgrounds, skills, and expectations. Additionally, triggering questions for the brainstorming session must be formulated. These questions should be of interest to the stakeholders and aligned with the organization’s identity (i.e., mission, vision, and values); in other words, they should also be of interest to the organization. Secondary research and direct observation (Liedtka & Ogilvie, 2019) are suggested to understand the organization, its stakeholders, and the things they value, to then formulate triggering questions.
• Discover Stakeholders’ Perceptions: In this phase, the key stakeholders of the educational facility are involved in discovering their dreams and expectations regarding the facility in a co-creation fashion. Blue-card brainstorming is suggested, alternating individual brainstorming sessions with collective analysis and sharing of the results (Liedtka & Ogilvie, 2019). This is a structured ideation technique that uses sticky notes (or cards) for individual idea generation in response to tailored trigger questions. Participants write down ideas silently—one per card—which are later shared and discussed collectively. This method ensures that all voices, including those of quieter participants, are heard and helps mitigate hierarchical dynamics often present in group settings. It has been shown to significantly increase the number and diversity of ideas generated compared to traditional brainstorming.
• Identify Insights: In this phase, the unstructured data obtained in the brainstorming session are converted into value drivers. In DT, the process of organizing the data by common themes is known as card sorting (Wood & Wood, 2008). Large Language Models (LLMs), such as ChatGPT, can be utilized in an iterative process between the software tool and the human, who provides context through effective prompts and validates the LLM results. The obtained insights must be aligned with organizational objectives to ensure they contribute effectively to strategic goals, foster innovation, and address the specific needs and opportunities identified.
• Build a Value Framework: In this phase, the value drivers are classified within the Industry 5.0 dimensions (i.e., human-centric, resilience, and sustainability) in the development of a VF for the upcoming educational facility. Classifying the value drivers within a more fundamental theoretical understanding of value can facilitate their assessment (Wijnia et al., 2022), ensure the definition of an Educational Facility 5.0, and enable the scalability of the initiative. Visualization, the transformation of information into images, is applied for its capacity to make ideas tangible and concrete (Liedtka & Ogilvie, 2011).

It is worth mentioning that, while Collaborative Analysis of data with the stakeholders is sometimes used in DT practices to promote empathy, reflection, and learning (Sanders & Stappers, 2008), it becomes increasingly difficult to apply during convergence phases—especially when large volumes of qualitative input are generated and many stakeholders are involved. For this reason, LLMs are employed to streamline the synthesis process. However, this efficiency comes with a trade-off: the potential reduction in opportunities to further foster collaboration and empathy during the analytical stages. To mitigate this risk, and although the process may appear linear (Figure 1), the resulting value drivers and frameworks are always validated—and, when necessary, iterated—with stakeholders. This ensures alignment, shared understanding, and a sense of ownership and empowerment within the community. Furthermore, we acknowledge that LLMs may carry biases inherent in their training data. To safeguard the integrity, inclusiveness, and pedagogical richness of the process, outputs are first reviewed and refined by facilitators familiar with the workshop context and then collectively discussed with stakeholders.

4. Case Study

The Clermont School (CS) is a Colombian educational institution associated with the Cambridge International Curriculum (https://www.clermont.edu.co, accessed on 26 July 2025).

Since 2018, CS has declared its commitment to educating students to be innovative, entrepreneurial, contribute to planetary sustainability, and see themselves as global citizens. To achieve a current and relevant education, CS has integrated research as a transversal axis of its curriculum and developed various innovations in infrastructure, pedagogy, and didactic methods. However, there was still a lack of facilities for the educational community to conduct research, engage in scientific activities, and produce new knowledge.

In exploring possibilities, CS came into contact in 2023 with the Universidad de los Andes (Uniandes) AgroLab (Zapata et al., 2019): a platform for dialogue, co-creation, and experimentation, integrating traditional, experiential, and technical knowledge to advance research, education, and awareness about food production, utilization, and interaction. By following the methodological phases indicated in Section 3, this section presents the collaboration between CS and Uniandes for the definition of the Clermont AgroLab: a facility able to dynamize the CS missionary and visionary educational objectives.

4.1. Define Focus Areas

We started the project with a comprehensive research phase to gather necessary insights. This included secondary research to explore references of agricultural laboratories as platforms for school education. Apart from the authors’ experience with the Uniandes AgroLab, various state-of-the-art laboratories and initiatives provided valuable insights into successful agricultural education platforms (Manning et al., 2022). Additionally, a visit was carried out in the CS to familiarize ourselves with the physical space and to establish an empathetic connection with the main stakeholders. This visit helped to understand the school’s identity, values, and aspirations, which are crucial for aligning the initiative with the CS vision.

Based on this understanding, we identified key CS stakeholders for the brainstorming session: students, professors, parents, and administrative employees. In this phase, only the roles were considered, while the specific individuals for each role were defined in the next phase. Furthermore, we selected the following focus areas to trigger participant engagement and ensure alignment with both the organizational identity and the successful agricultural educational facilities referenced:

• Space: Considers not only the physical environment but also the emotional, behavioural, and symbolic attributes of the educational facility.
• Food: Addresses the entire food cycle within and outside the educational facility, including aspects related to food habits, sustainability, and research opportunities, among others.
• Education: Relates to the educational goals and methodologies that will be employed within the facility.
• People: Focuses on the envisioned role of humans with respect to the upcoming facility, including interactions, responsibilities, and community engagement.

Therefore, four triggering questions were defined as follows:

What is your dream for the Clermont AgroLab concerning ‘i-th Focus Area’?

4.2. Discover Stakeholders Perceptions

To discover the stakeholders’ perceptions, a blue-card brainstorming workshop (2 h) was designed as depicted in Figure 2. The workshop consists of the following moments:

• Context (20 min): A 15 min presentation is prepared to set the context for the brainstorming session. It begins with the CS principal explaining the purpose of the workshop and ensuring the institution’s endorsement and commitment to the initiative. Following this, the Uniandes professors present the identified focus areas, explain the process for their definition, and showcase successful agricultural educational facilities, including the Uniandes AgroLab, to inspire and prepare participants for brainstorming. In the remaining 5 min, groups are formed.
• Individual Brainstorming (2 min per focus area, 20 min in total including buffer time): Each participant individually brainstorms their dreams concerning the educational facility across the identified focus areas, creating a sticky note for each idea. This fast process encourages creative thinking by requiring immediate responses without extensive reasoning, connecting more to emotions, instincts, and spontaneity. In the DT inspiration phase, spontaneity is particularly useful as it can foster enjoyment and creativity, reduce cognitive load, and facilitate divergent thinking, which leads to a broader range of ideas and solutions (Viola, 2023).
• Open Card Sorting (5 min per focus area, 30 min in total including buffer time): In groups, participants review the developed ideas, organize them by common themes, and assign distinct names to each theme (Wood & Wood, 2008). This step provides context for the ideas, addressing the issue of some sticky notes lacking context due to the limited time available, which was essential during the processing of the obtained data.
• Break (10 min): A short break to allow participants to relax, recharge, and informally discuss their ideas before proceeding to the next phase.
• Sharing (40 min): Each group spends 15 min preparing a 3 min presentation to share their results and their experiences regarding the brainstorming session. This activity adds further context during the data processing phase and helps to capture what stakeholders value most. This exercise also ensures that once the data processing results are presented, stakeholders see that the outcomes are derived from their own inputs and those of other groups, fostering empowerment and collective ownership of the results.

Before the workshop, the required number of CS participants per role was specified (i.e., four from each role: students, professors, parents, and administrative employees). The workshop, held on 15 November 2023, at CS (Figure 3), included 16 participants forming four mixed groups. These cross-functional teams were created to bring diverse perspectives to each group, fostering comprehensive discussions and enhancing the creativity and quality of the ideas generated.

Although these sessions typically include an ice-breaking exercise and a voting session to select the best ideas, time constraints prevented us from incorporating these elements. Additionally, we considered using sticky notes of different colors to trace the origin of each idea by role, but limited resources led us to exclude this during the data processing phase.

A facilitator was assigned to each group, and some sweets were provided. Having sweets on the table can improve mood, foster a positive atmosphere, enhance motivation through rewards, and help maintain energy levels, overall stimulating creativity and engagement (Xu et al., 2022). Additionally, one person was responsible for managing the overall workshop logistics and ensuring the schedule was adhered to.

4.3. Identify Insights

This section describes the identification of the Clermont AgroLab value drivers through the utilization of the open card sorting technique with the support of LLM. This process consists of the following:

• Digitalization: The 361 sticky notes generated during the workshop were digitized into Excel, along with the themes defined by the groups. Unlike the brainstorming session, where the notes were divided into four groups, all sticky notes were treated as a single group for this process. The themes identified during the workshop added valuable context to the sticky notes, as some of them would have been difficult to interpret in isolation. However, the open card sorting conducted during the workshop resulted in numerous and overlapping categories. While these categories provided additional context, they did not facilitate effective information synthesis.
• Open Card Sorting: to synthesize the information and define value drivers, we utilized ChatGPT 3.5 in an iterative process with human supervision, as illustrated in

  Table 1. Interaction process between human and LLM (ChatGPT) for open card sorting and value driver definition.

  Function	Prompt	Input Data
  Context	I need to process the data from a workshop conducted at Clermont School in Bogotá to identify the needs for creating an agricultural lab (AgroLab). The goal is to view agriculture as a platform to learn various disciplines and beyond. The workshop aimed to discover needs, opportunities, and dreams related to introducing an AgroLab at the school. Can you help me process the information?	/
  Open Card Sorting	I need help with an open card sorting exercise to generate value drivers from the workshop results. Value drivers are elements deserving proper control because they significantly influence the realization of the company’s value. These value drivers will help us identify needs, opportunities, and dreams related to the AgroLab’s introduction at the school. The brainstorming exercise was structured around four triggering questions: space, people, education, and food. For each question, we will classify the obtained information into value drivers. Let us start with the i-th question. Here are the sticky notes. Can you classify them into value drivers?	Sticky notes from the i-th triggering question
  Definition	Can you generate a definition for the value drivers defined by the i-th triggering question considering their sticky notes?	i-th triggering question, defined value drivers, and corresponding sticky notes

  . The LLM grouped the sticky notes by similar subjects and assigned a name and definition to each value driver. We also tracked the number of sticky notes for each value driver, as this number can indicate its relevance to the workshop participants. Despite the straightforward nature of the exercise, ChatGPT occasionally overlooked some sticky notes. Additionally, being present at the workshop was beneficial for adjusting the results according to the dynamics observed during the sharing phase. While these limitations did not compromise the overall validity of the synthesis, they highlight the importance of human oversight in ensuring that no relevant ideas are excluded and that the nuanced contributions of participants are accurately represented. This discussion will be further elaborated in Section 5.
• Overall Picture: Once the value drivers were identified, they were plotted in Excel alongside the focus areas to create a comprehensive visualization of the results (Figure 4). Analyzing the image revealed that the focus areas had a similar number of sticky notes and that some value drivers were common or similar across different areas.
• Information Synthesis: Given these insights, it was decided that categorizing by focus areas was no longer necessary. Furthermore, common value drivers were merged into a single definition, encompassing the sum of the individual sticky notes. For this, ChatGPT was used again with the following prompt: ”I will provide you value drivers with their corresponding definitions. Can you synthesize them into a single value driver with one definition?” An iterative process followed until the results were consistent with the discussions of the brainstorming session.
• Strategic Alignment: The obtained value drivers were then compared with the CS organizational objectives. Minor modifications were made to enhance the alignment in terms of wording. However, the value drivers were largely in line with the CS organizational objectives, demonstrating that the educational community had internalized these goals. Had this not been the case, iterations would have been necessary to review the extent to which the new educational facility should align with the CS strategic objectives.

4.4. Build a Value Framework

Industry 5.0 represents a paradigm shift that emphasizes the collaboration between humans and advanced technologies, aiming to create a more sustainable, resilient, and human-centric industrial landscape. According to the European Union (Cotta et al., 2021), human-centric refers to an industry that promotes talents, diversity, and empowerment; resilient indicates agility and adaptability with flexible technologies; and sustainable means leading action on sustainability and respecting planetary boundaries. Although these dimensions are defined for industries, it made sense to adopt them for educational facilities as they embody desirable properties for these environments as well. Therefore, the obtained value drivers were mapped within the Industry 5.0 dimensions for the building of the Clermont AgroLab Value Framework.

The resulting VF was then presented to the CS top management, along with a detailed explanation of the process used to develop it. Although minor modifications were made to some terms, the final outcome aligned with the initial expectations and objectives and was subsequently shared with the educational community. The results resonated well with all stakeholders, likely due to the co-creation process. Furthermore, the educational community felt a sense of pride in the result and demonstrated a strong commitment to the next steps, as they felt ownership in ‘building their own AgroLab’.

5. Results and Discussions

This section first presents the obtained results (Section 5.1) and then discusses the developed transdisciplinary methodology (Section 5.2). Following this, the role of Social Innovation and Design Thinking in Asset Management (Section 5.3) is examined, along with the role of Large Language Models in convergent processes (Section 5.4). Finally, a vision for Educational Facilities 5.0 is outlined (Section 5.5).

5.1. The Clermont AgroLab

Figure 5 presents the value drivers of the Clermont AgroLab, ordered based on the number of sticky notes that contributed to their definition. By analyzing the image, it can be seen what the community values most, offering guidance and support for decision-making regarding the subsequent phases of the facility lifecycle.

Furthermore, this analysis can act as a tool to check alignment with organizational objectives. For instance, while ‘Research and Entrepreneurship’ holds a significant role within the CS organizational objectives, this importance is not reflected in the results of Figure 5. Therefore, CS should consider placing greater emphasis on this driver within the educational community or reassess whether it should be a key driver for the upcoming facility. Additionally, the ‘Impact’ value driver received fewer sticky notes because, in the authors’ opinion, the impact is more of a consequence of all the other elements combined rather than a standalone driver.

‘Community Participation’ and ‘Teaching Methodologies’ received the highest ranks. This is typical in SI initiatives, where participants feel ownership of the results and are motivated to engage more in this co-creative and co-design manner. Although this is common in SI, it is less so within the AM community. In fact, such ownership constitutes a remarkable AM result, as integrating the facility into educational operations enables the community itself to contribute to the long-term viability of the facility’s operations.

In

Table 2. Definitions of the value drivers for the Clermont AgroLab.

Value Driver	Definition
Community Participation	The AgroLab fosters interaction, active collaboration, and the capacity for agency among all members of the educational community, including parents, students, teachers, and staff. Additionally, it seeks to integrate external actors, mutually enriching each other’s knowledge and experiences. This is achieved through assertive, timely, and oriented communication.
Teaching Methodologies	The AgroLab employs educational approaches that stimulate creativity and active learning by incorporating practices that promote freedom of creation, experimentation, and interaction with nature. Additionally, students harmoniously integrate their various areas of development through active methodologies such as constructivism, problem-based learning, and cross-curricular and classroom projects. This creates an inclusive, interdisciplinary, and motivating environment for students.
Environmental Sustainability	The AgroLab is a space committed to environmental awareness and sustainability, both in construction and in food production and transformation. Priority is given to the use of recycled materials, biodiversity, and sustainable agricultural techniques that combine technology and experiences to promote environmental care and the generation of healthy, nature-friendly foods.
Emotional Well-being	The AgroLab is designed to promote inclusion, relaxation, and emotional well-being. In this space, diversity is celebrated, and a peaceful and welcoming environment is created that invites dreaming and escaping from routine. Additionally, it seeks to establish an emotional and practical connection with the surroundings, thus contributing to the creation of a sense of belonging and mutual respect.
Food Awareness	The AgroLab promotes conscious and healthy eating, encouraging understanding of the importance of food variety, nutritional education, and respect for the origin and diversity of foods, prioritizing physical and mental health.
Personal Development	The AgroLab contributes to the integral development of students by promoting the manifestation of Clermont-Cambridge attributes and achieving the formative ideals of the institution. Additionally, it fosters assertive and empathetic communication, self-management, and emotional development in the community.
Professional Competences	In the AgroLab, students acquire and develop a set of knowledge, skills, and attitudes throughout their education. These include teamwork, environmental awareness, problem-solving, leadership, research, and critical thinking, among other fundamental aspects for personal and professional development.
Research and Entrepreneurship	The AgroLab fosters creativity, innovation, and socially responsible entrepreneurship in the food field, from the implementation of new agricultural practices to the conception of innovative projects to address community needs. It prioritizes diversity and food excellence, becoming a space for generating new knowledge and creating innovative products and services.
Impact	The AgroLab contributes to the education of students to become global citizens, leaders, competent, and capable of transforming reality for the betterment of society.

, the reader can find the definition of each value driver. These definitions resulted from the iterative open card sorting process facilitated by ChatGPT, coupled with the authors’ contextual insights from the workshop activities. Additionally, minor modifications were made to ensure alignment with organizational objectives and incorporate feedback from the presentation to the CS top management.

Based on these definitions, we mapped the value drivers within the Industry 5.0 dimensions to build the Clermont AgroLab Value Framework, as depicted in Figure 6. The mapping was straightforward, with one exception: the resilient dimension. In this work, resilience refers to the capability to rapidly adapt to changes in the external environment and maintain operations. Given this, the Clermont AgroLab is likely to remain resilient even in the face of external challenges such as financial cuts, as it becomes part of teaching methodologies, a tool for developing professional, research, and entrepreneurship competences, and is actively operated and maintained by the educational community. Therefore, community participation and the integration of lifecycle delivery activities within educational operations are recognized as key enablers of resilience. While this approach is state-of-the-art in SI, it represents a remarkable outcome for the AM community.

Finally, a visual VF was built (Figure 6) since visualization substantially reduces project risks in two ways (Liedtka & Ogilvie, 2011): (i) Interpretation: When ideas are explained using text, people form their own mental pictures. Presenting the idea through a picture or story reduces the likelihood of unmatched mental models. (ii) Commitment: The clearer the community visualizes their desired future facility, the more likely they are to persevere through execution challenges. The visual VF can be printed and distributed within the educational community to build pride, empowerment, commitment, and alignment. It provides common ground for discussion and effective guidance for decision-making regarding subsequent facility lifecycle phases.

5.2. Transdisciplinary Methodology

The proposed methodology can be classified as transdisciplinary in accordance with the framework outlined by Wickson et al. (2006). First, it is rooted in a strong societal Problem Focus, addressing the complex and real-world challenge of defining Educational Facilities 5.0—resilient, human-centric, and sustainable learning environments. Second, it involves the integration and fusion of multiple disciplinary perspectives, namely SI, AM, and DT. These are not applied in parallel or sequence but are interwoven into a context-sensitive and Evolving Methodology, making disciplinary boundaries fluid and indistinct. Third, the approach is inherently Collaborative, involving not only researchers and practitioners but also members of the educational community as co-creators. By incorporating both theoretical foundations and practical insights, the methodology transcends traditional academic silos and generates actionable, stakeholder-informed solutions that reflect and respond to real educational needs. In this context, it is worth noting that while much of the literature frames transdisciplinarity as a means to address problems (Lattanzio et al., 2021), it can—and should—also be applied to pursue opportunities. For example, the development of the AgroLab at CS represented a proactive opportunity rather than a response to a specific problem. As Keeney (1996) points out, problems are typically reactive responses to existing deficiencies, whereas opportunities are forward-looking initiatives aimed at creating new value.

Grounded in these transdisciplinary principles, the proposed methodology reveals key benefits when applied to the Clermont case study, illustrating its capacity to generate meaningful, actionable outcomes. Specifically, these advantages include:

• Educational Facility 5.0: Educational Facility 5.0 is defined by classifying value drivers within the three dimensions of Industry 5.0, i.e., human-centricity, resilience, and sustainability. In addition to the well-being and empowerment emphasized by Industry 5.0, we consider that the human-centric dimension in the educational context also encompasses the application of student-centered pedagogical theories such as constructivism, experiential learning, and project-based learning. These pedagogical frameworks emphasize active engagement, collaboration, and real-world problem-solving—competencies that are central to 21st century education. By embedding these principles into the facility’s design and function, the methodology supports not only infrastructure transformation but also pedagogical innovation. Furthermore, the utilized SI-driven process fosters community ownership and ensures the facility is meaningfully integrated into educational operations, enhancing both its adaptability and long-term resilience.
• Asset Management Knowledge: Building on Ackoff’s hierarchy (Ackoff, 1989), data, information, and knowledge are understood as distinct stages in the decision-making process, each representing an increasing level of contextualization and understanding. Data refers to raw, unprocessed facts; information is data that has been contextualized and organized, and knowledge is the actionable understanding derived from that information to inform decisions. In this context, the proposed methodology facilitates the transformation of raw data (the sticky notes) into information (the value drivers), and ultimately into knowledge (the prioritization of value drivers by frequency). As a result, the methodology enables the representation of community preferences into AM Design Knowledge, offering structured guidance and supporting decision-making in the subsequent phases of the facility’s lifecycle.
• Cultural Change: Achieving an Educational Facility 5.0 requires cultural shifts, particularly in motivating the educational community to actively support the facility’s long-term operational viability. Kotter’s Eight-Step Change Model can be applied to guide this transformation (Ganz et al., 2010). By mapping Kotter’s model with the transdisciplinary methodology of this article, it can be noticed that the proposed approach fulfills the first four steps of the model: establishing a sense of urgency, building a guiding coalition, developing a vision, and communicating that vision.

When implementing this transdisciplinary methodology, two primary risks must be carefully considered to ensure its effectiveness:

• Alignment: In Kotter’s words, a guiding coalition consists of individuals with enough power and influence to lead the change effort. Additionally, in our opinion, participants in these co-creative and co-design exercises should have a ‘transdisciplinary attitude’; i.e., be empathetic and open to learning from others. In DT, these individuals are often referred to as having a ‘beginner’s mindset’ (Brown & Katz, 2011), meaning they approach topics with curiosity and interest, free from the constraints of traditional, narrow perspectives. They tend to avoid confusing their own beliefs with the actual truths about the environment. Facilitators play a fundamental role in nurturing this mindset, guiding participants to engage constructively and collaboratively. If individuals lacking these characteristics are selected, achieving alignment can become challenging.
• Commitment: The enthusiasm generated among participants during the facility definition phase may wane over time or in the face of early challenges. Future work will investigate how to enhance the proposed methodology to sustain engagement and ensure long-term commitment to the initiative throughout the development and implementation phases.

5.3. Social Innovation and Design Thinking in Asset Management

Many social initiatives fail when implemented solely in a ‘top-down’ fashion, particularly when the needs, values, and contexts of the community are not properly understood or integrated into the solution. A well-known example is the Solar Water Heating Programme in South Africa, where the lack of community engagement and understanding of local conditions led to limited adoption and eventual failure (Netshiozwi, 2019). Such initiatives often falter because they overlook the importance of including the voices of those most affected in the decision-making and implementation process.

A similar disconnection is often observed in AM, where misunderstandings, conflicting priorities, and missed opportunities frequently arise between lifecycle delivery teams and top management; shown on the left side of Figure 7. The complex, ‘wicked’ problems resulting from this disconnection are typically difficult to resolve and are often classified under the umbrella of challenges related to leadership and organizational culture. This is further validated by a recent global survey conducted by the GFMAM (Global Forum on Maintenance and Asset Management), where 506 maintenance and AM practitioners identified leadership and culture as the most critical factors for sustaining initiatives in the era of digital transformation (GFMAM, 2024).

As indicated on the right side of Figure 7, Asset Management frequently serves as a mediator between these two groups, standing at the intersection of the desires and expectations of top management and the realities and opportunities presented by the assets and AM capabilities (IAM, 2021). Although the necessary skills for AM teams have been well defined (Cripps & Wallsgrove, 2020; IAM, 2014), there remains a gap in effective methodologies and tools to bridge and manage the conflicts that frequently arise between top-down directives (from management) and bottom-up input (from lifecycle delivery teams).

In this context, Social Innovation offers a promising approach to addressing this disconnection by fostering more inclusive, bottom-up methods. Unlike top-down initiatives, which are often led by centralized decision-makers, SI emphasizes active community participation, co-design, and empowerment. It promotes the idea that the people most affected by a decision should have a significant role in shaping the solution. In this regard, this approach has already been implemented in Total Productive Maintenance, demonstrating that involving operational staff in tactical maintenance-related decisions not only enhances the overall effectiveness of maintenance plans but also improves the rate of elimination of defects that can potentially lead to asset failures or performance inefficiencies (Hartmann, 1992; Winston et al., 2012).

While SI applied to AM fosters dialogue and collaboration between top management and lifecycle delivery teams, the risk of conflict and misalignment may still persist. This is where Design Thinking’s human-centric and empathetic approaches help bridge the gap between top-down and bottom-up, fostering mutual understanding of the needs, concerns, and motivations of all the involved stakeholders.

This work demonstrates how the proposed transdisciplinary methodology, integrating AM and SI through DT, fostered a sense of ownership within the educational community toward the agricultural laboratory. It also inspired a desire to integrate the facility into educational operations, enabling the community to contribute to its long-term viability. This approach seems promising not only for the development of new assets but also for enhancing asset-related decision-making. By fostering a cross-functional and empathetic work environment, it can help break down traditional silos, distribute responsibilities more effectively, and ultimately transform the organization into one with shared values and visions, as shown on the right side of Figure 7.

However, SI and DT approaches are time- and resource-intensive, making their application to all asset-related decision-making impractical. Therefore, Figure 7 illustrates how, in the authors’ opinion, these elements should be integrated into AM proportionally to the decision’s impact. The more strategic the decision, the more time should be allocated to forward-thinking and engaging relevant stakeholders through SI and DT’s human-centric and empathetic approaches.

5.4. Large Language Models in Convergent Processes

The topic of using Large Language Models in convergent processes is relatively new, and in this section, we aim to share our reflections on their potential impact and application.

SI and DT approaches alternate between divergent and convergent processes, as they involve gathering input from various stakeholders and converting it into actionable insights. As a result, the volume of collected data can be substantial. For example, in this work, 361 sticky notes were collected during a two-hour workshop, capturing a wide range of ideas and perspectives from the 16 participants. This highlights the need for tools that can efficiently process and synthesize large amounts of information.

An effective tool for synthesizing insights from such a high volume of data is LLMs. Compared to traditional qualitative analysis software such as NVivo and ATLAS.ti, LLMs are more intuitive and accessible to a broader audience, enabling faster data processing without requiring specialized knowledge. Additionally, LLMs offer a more flexible approach by responding to dynamic prompts, making them adaptable to various contexts and capable of processing nuanced language inputs, which can enhance the quality of insights generated. Compared to manual data processing, they can help ensure objectivity by reducing human biases or errors, such as data being overlooked due to fatigue. Moreover, LLMs play a crucial role in handling repetitive tasks, enabling teams to dedicate more time and focus on higher-level reflections of the draft insights generated by the LLMs, ultimately adding greater value to the process. Thus, LLMs provide a practical solution for streamlining the convergence process and turning unstructured data into meaningful insights.

While LLMs are helpful in assisting with data convergence, they cannot fully replace human involvement. In this work, being present during the workshop provided valuable context that was critical for refining and adjusting the LLM’s output based on the dynamics and conversations observed. Additionally, aligning the insights with the company’s strategic objectives required human expertise. Therefore, we view LLMs as highly effective assistants, but they must be guided with precise prompts, and their outputs must be reviewed and critically analyzed by humans.

In many organizations, LLMs are already being used formally and informally for various tasks. While their potential is being explored in sectors such as education (Kasneci et al., 2023) and software engineering (Hou et al., 2024), there is an increasing need for studies that address the business perspective. Works like the one presented in this article can help clarify what LLMs can and cannot do, how human–machine interactions should be structured (e.g., crafting effective prompts), and how these technologies can be integrated within business processes.

However, when employing LLMs, it is important to consider the ethical implications, especially when sensitive data is involved. In this work, ethical concerns were minimal, as no sensitive data was processed. In the application of LLMs, special attention must be given to issues such as data privacy, transparency, and accountability to ensure ethical standards are upheld. Establishing clear guidelines for LLM use in processes involving personal or sensitive information is crucial for maintaining ethical integrity.

Finally, we argue that LLMs and artificial intelligence (AI) can enable a breakthrough transformation in SI at any scale. A compelling example of how these technologies can support large-scale convergence and facilitate SI comes from the political domain. In 2025, the state of Guanajuato, Mexico, developed its official Government Program using AI to process extensive qualitative data collected from workshops, interviews, and technical documents (https://boletines.guanajuato.gob.mx/2025/02/13/guanajuato-hara-historia-con-el-primer-programa-de-gobierno-realizado-con-ia/, accessed on 17 July 2025). This case illustrates how LLMs can serve as enablers of inclusive and participatory innovation processes at scale. By managing and synthesizing large volumes of unstructured qualitative input, LLMs extend the applicability of DT and SI methodologies into policy-making and public administration. This reinforces the notion that, when ethically and strategically applied, LLMs hold significant potential to usher in a new era of Social Innovation—one characterized by greater scalability, inclusivity, and accessibility.

5.5. Educational Facilities 5.0

Since the main objective of this work was to investigate the extent to which the Industry 5.0 framework can be integrated into educational institutions, this section presents a vision for Educational Facilities 5.0. This vision embodies a transformative approach to learning environments that prioritizes resilient, human-centric, and environmentally sustainable facilities and practices. At its core, Educational Facilities 5.0 consists of spaces that not only facilitate academic achievement but also foster holistic development, community engagement, and adaptability to future challenges.

The concept of resilience in Educational Facilities 5.0 encompasses several key dimensions that are essential for creating adaptable and sustainable learning environments. Firstly, Physical Resilience can be intended as the facility’s flexibility to modify its configuration in response to changing needs and learning requirements. Secondly, Operational Resilience emphasizes the importance of maintaining effective learning operations during unforeseen challenges, including financial constraints or sudden shifts in educational demand. Thirdly, Social Resilience highlights the role of community engagement and ownership, fostering a supportive network that encourages collaboration among stakeholders, including students, parents, educators, and other institutions. Lastly, Technological Resilience involves integrating innovative technologies that can adapt to evolving educational paradigms, enabling the facility to leverage new tools and methods for enhanced learning experiences.

The concept of human-centric approaches in Educational Facilities 5.0 centers on creating inclusive, supportive, and engaging learning environments that prioritize the needs and well-being of all individuals within the educational community. In these environments, scientific and humanistic knowledge seamlessly integrate to solve local challenges, and art is utilized to enhance the effectiveness and impact of learning messages. The facilities are also designed to develop 21st century skills such as leadership, with older students taking on roles as trainers for younger peers. By fostering a culture of empathy, collaboration, and co-creation, Educational Facilities 5.0 aims to empower individuals to actively participate in their educational journeys and contribute to the ongoing development of the facility.

A commitment to environmental sustainability will be a cornerstone of Educational Facilities 5.0. These spaces will be designed to minimize their ecological footprint through renewable energy sources, the use of recycled materials, waste reduction strategies, and sustainable resource management and circular economy practices. By modeling environmentally responsible behaviors, educational facilities will not only contribute to a healthier planet but also instill a sense of environmental stewardship in students.

To realize our vision for Educational Facilities 5.0, it is essential to recognize that no single institution can achieve this ambitious goal in isolation. In the open-source era, a collaborative approach is vital. This vision can be brought to fruition by establishing a network of educational institutions and organizations with varying levels of maturity, all committed to exchanging knowledge, methodologies, and tools for developing and operating Educational Facilities 5.0. By fostering partnerships and sharing best practices, these institutions can collectively enhance their capabilities and resources, creating a synergistic effect that accelerates innovation and development. An illustrative example of this collaborative approach is depicted in Figure 8 for a potential AgroLab network.

As a final reflection, we believe that the technologies, physical assets, pedagogical methods, and management practices necessary for innovating educational institutions are already available. Therefore, the current task is to reimagine existing narratives and design new forms of human interaction and engagement for future learning. While SI and DT methodologies are valuable in this creative process, integrating AM practices will enable the development of sustainable educational facilities and models.

6. Conclusions and Future Work

As educational institutions grapple with the complexities of modern learning demands, it becomes evident that a transformation is essential. The integration of the Industry 5.0 principles into education presents a unique opportunity to enhance educational practices and learning environments. By embracing this framework, institutions can leverage human creativity alongside advanced technologies to create dynamic and adaptive learning spaces. In this context, this article presented a transdisciplinary methodology that integrates AM with SI through DT to co-design resilient, human-centric, and sustainable educational facilities 5.0.

The proposed methodology demonstrates three main contributions through its application to a case study. First, the methodology facilitates the definition of an Educational Facility 5.0 by classifying insights from the educational community within the dimensions of Industry 5.0 and employing a participative, co-creative approach. Second, it allows for the identification of AM Design Knowledge, providing structured guidance that supports decision-making in the subsequent design, implementation, and operation phases. Third, Alignment for Cultural Change is achieved by fostering community ownership, which inspires the integration of the facility into educational operations and enhances its resilience against external challenges.

Given the novelty of the approaches and tools employed, and in line with DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this work also discussed the role of SI and DT in AM, the role of LLMs in convergent processes, and a vision for Educational Facilities 5.0. In particular, Social Innovation and Design Thinking are viewed as ’lubricants’ that help AM address the frictions that tend to arise between top management and lifecycle delivery teams; their utilization should be proportional to the impact of the decision. Large Language Models are identified as highly effective assistants in convergent processes, helping to ensure objectivity by reducing human biases and errors. Their integration signals the emergence of a new era of SI—one marked by greater scalability, inclusivity, and accessibility—where AI technologies like LLMs extend the reach of participatory design and support systemic transformation across sectors. Finally, a network of educational institutions and organizations is envisioned as a means of developing resilient, human-centric, and sustainable Educational Facilities 5.0.

The proposed transdisciplinary methodology lays the groundwork for a new approach to educational facility design and opens several promising avenues for future research and refinement. The main directions identified are as follows:

• Value Metrics: In Value Frameworks, value metrics must be assigned to the identified value drivers. Techniques for building performance measurement systems may be explored for this purpose, along with existing Industry 5.0 frameworks.
• Development and Implementation: This work focused solely on the definition of an Educational Facility 5.0. Future research should expand this to include the development and implementation phases.
• AgroLab Network: Explore the potential for creating a broader network of AgroLab Facilities 5.0 across various educational institutions, allowing for the sharing of best practices, resources, and innovative pedagogical and management methodologies.
• LLMs in Divergent Processes: While this study focused on the use of LLMs for data convergence, emerging research is beginning to explore their potential in divergent phases as well. Notable applications include the creation of digital personas and the simulation of stakeholder interviews and workshops. These examples suggest a promising role for generative AI in expanding idea generation and fostering broader participation in SI-driven processes.