Developing Technical Literacy for Business School Students Studying Innovation

Abstract

This study examines how business school students with no programming background develop technical literacy through a newly introduced Digital Innovation course. Addressing a gap in non-STEM education research—where little is known about how social science students experience technical literacy interventions—we draw on qualitative data from group exam reflections (n = 14) and mid-semester survey responses (n = 7). Using an inductive thematic analysis, the study investigates how students perceived, navigated, and made sense of foundational coding activities. Four themes emerged: (1) Perceived value of coding and technical literacy, (2) Hidden gaps in foundational technical literacy, (3) AI as a cognitive and pedagogical scaffold and (4) Emerging technical competence and identity formation. Framed within theories of digital literacy and constructivist learning, the findings show how limited, scaffolded exposure to web development can shift students from digital consumption toward novice digital production. The study contributes empirical insight into how coding can be meaningfully embedded within business school curricula and offers pedagogical recommendations for designing accessible technical literacy interventions.

1. Introduction

In an era where technological advancements continuously reshape the landscape of work and education, the need for relevant skills has never been more critical (Hinterplattner et al., 2025; Moorhouse et al., 2024). Technical literacy, encompassing skills in understanding, using, and applying technology, has become increasingly important (Allen, 2020; Familoni & Onyebuchi, 2024; Ritz, 2011). In contemporary higher education, cultivating 21st-century skills has become paramount, including competencies such as critical thinking, creativity, collaboration, communication, and, notably, technical literacy (Thornhill-Miller et al., 2023; Voogt & Roblin, 2010). Recent reviews emphasise that coding is increasingly understood as a cross-disciplinary means of developing technical literacy, yet remains underdeveloped in non-STEM programmes (Mills et al., 2025). Evidence from business-focused higher education contexts further suggests that students’ everyday use of digital technologies often does not translate into technical literacy (Mentzer et al., 2024).

Prior research challenges the assumption that contemporary students are inherently digitally competent. Lamia (2024) demonstrates that young people’s digital skills are uneven and frequently limited to consumption-oriented practices, suggesting that educational institutions may overestimate students’ technical competence. Non-STEM students demonstrate significantly lower and more uneven levels of digital literacy compared to STEM students, with students in management-related programmes exhibiting particularly low levels (Saetang et al., 2023; Das & Bhattacharyya, 2023). These differences are primarily attributed to curricular exposure rather than age or prior technology use, highlighting the role of education in shaping students’ digital competence.

There is a well-developed body of research examining technical literacy and coding within STEM education (Govender, 2025; Mills et al., 2025), while comparable studies remain scarce in non-STEM domains such as business and innovation education, where coding is not traditionally part of the curriculum. To date, the literature offers limited guidance on how foundational technical skills can be meaningfully integrated into such programmes (Kayyali, 2024). Given this scarcity of research, there is a need for accounts of how foundational technical skills, such as coding, can be introduced and how they are experienced within business and innovation education.

Against this backdrop, we observed an absence of educational content dedicated to developing students’ technical literacy within the programmes we taught. To address this, we created a novel course, BED3101 Digital Innovation, aimed at equipping students with essential technical skills for navigating the digital age within the context of an innovation course. This paper focuses on the development and implementation of the Digital Innovation course and its role in addressing the need for technical literacy among social science students in higher education.

The paper offers a reflective account of our experiences as educators and the feedback from students who participated in the course. The objective is to provide insights and practical guidance for other educators within the social sciences seeking to enhance technical literacy within their programmes. It might be of particular interest to educators who see a need for technical competence in their students but are unsure how to initiate such a course, particularly if, like us, they feel that technology lies outside their core competence.

2. Background

Technology is increasingly being integrated into social science education, often through dedicated courses or as components of interdisciplinary programmes (Moorhouse et al., 2024). These initiatives typically aim to provide students with a broad understanding of technological concepts, rather than focusing on hands-on technical skills. While this can offer valuable insights, it may also result in a surface-level grasp of how technologies actually function. In designing the course, we aimed to go beyond this, fostering a deeper engagement with the underlying principles of technology—particularly through coding. Coding serves as a foundational element across a wide range of technologies, and developing a working knowledge of it can support more nuanced and critical perspectives on technological systems. The goal is not simply to teach students how to code, but to use coding as a means of unlocking a broader, more informed understanding of the digital world.

After enquiring into various teaching approaches, including those used by other institutions, we concluded that web development offers a broad and multifaceted introduction to the field, aligning with research by Mendez (2014). This method provides students with immediate visual feedback, aiding the learning process by allowing them to see errors on the monitor and observe the outcomes of their code in real-time. While many universities teach Python (both version 2 and 3) due to its popularity (Javed et al., 2019; Guo, 2013), we chose web development for several reasons. One key consideration was the availability of teaching staff with expertise in web technologies. Having an instructor proficient in web development ensures quality instruction and support for the students, enabling us to offer a course of this nature.

We believed that developing applications and handling web data would be both useful and pedagogically engaging for students. Web development skills are directly applicable to many business contexts, such as creating interactive websites, managing online data, and understanding web-based systems fundamentals, which are increasingly relevant in the digital business world (Ben Youssef et al., 2022). Web tools and platforms are prevalent in students’ daily lives. Understanding the interaction between frontend and backend, and database functionality, provides a foundational grasp of technological systems, from apps and websites to IoT devices.

Web development is also pedagogically advantageous, as creating the first website is straightforward and offers quick, tangible results, which are important for maintaining student engagement and motivation (Aziz et al., 2023; Nisser et al., 2024). Although developing complex web applications remains challenging, web development provides an accessible and engaging entry point. It offers early successes and a broad understanding of numerous technical concepts, along with the potential for deeper, more complex projects.

Ideally, business students should learn various coding skills, including Python for data analysis and visualisation, among others. Combining Python’s powerful data handling capabilities with web development would allow students to present their analyses on interactive websites, offering them a comprehensive skill set. However, given the limitations on the number of technical courses that can be included in the programme, we must start somewhere. Web development provides a strong foundation and an engaging entry point into coding and technical skills.

By reflecting on the outcomes of the Digital Innovation course, we aim to offer insights that may help other educators make informed decisions about how technical literacy can be meaningfully incorporated into business education. Our experiences—both the challenges encountered and the successes observed—contribute to broader discussions on the role of programming, digital competence, and applied technological skills within non-technical higher education programmes. In sharing these reflections, we also seek to illustrate how our pedagogical approach supported students’ engagement with unfamiliar technical material and to provide a basis for educators considering similar course designs.

The study is exploratory, and our analysis focuses on how students themselves described their perceptions of the course, the processes through which they navigated its learning activities, and the types of development they felt the course enabled. Rather than beginning with predefined hypotheses or fixed analytical categories, the project was guided by an open interest in understanding how students experience a learning environment that requires the acquisition of new technical competencies. This orientation allowed themes to emerge directly from the students’ written reflections, capturing their attitudes toward the course, the challenges and strategies that shaped their learning process, and the learning outcomes they associated with their participation. These perspectives form the foundation for the thematic structure presented in the Section 5.

2.1. Course Participants

The course was first offered in the spring of 2024 and was attended by a relatively small cohort of 14 students. This course was scheduled in the first semester of the master’s programme in Leadership, Innovation and Marketing.

The educational backgrounds of the participants were evenly divided between those who had completed the [redacted] bachelor programme and those with a bachelor’s degree in Business and Administration. Notably, only one student in the class reported having minimal programming experience from high school. Consequently, 13 out of the 14 students had no prior experience with coding.

2.2. Course Design and Implementation

The course, Digital Innovation, is structured into two distinct modules. The first module adopts a traditional approach, where students learn models, theories, and methodologies commonly used in digital innovation projects. Specifically, the curriculum covers Design Thinking, Agile Development, and Lean Startup. The deliverable for Module 1 was a low-fidelity prototype of a digital innovation, developed based on one or more of these methodologies. The first module provided an important foundation for what the students would later attempt to code. The pedagogical value of Design Thinking, Agile and Lean Startup has been well covered in other work, and is therefore not the main focus of this article.

The second module was focused on coding, and on bringing the students’ low-fidelity prototypes to life. It is the main focus of our research, as we believe this kind of course is still relatively novel within business school contexts. Here is a brief overview of the structure and content covered in Module 2:

Web Introduction: Module 2 started with a brief introduction to the web. This included how the internet works, explaining the relationships between clients, servers, internet service providers (ISPs), and web pages. Additionally, it covered the use of protocols like HTTP and HTTPS, as well as FTP and the secure alternative SFTP for sending and receiving documents and data over the web.

HTML Section: Students were introduced to HTML, including basic tags and attributes. They learnt how to structure a web page using elements such as headings, paragraphs, links, and images.

CSS Section: The course then transitioned to CSS, where students learnt basic styling and ways to link CSS with HTML. They were introduced to foundational styling methods, including setting colours, fonts, and spacing. Additionally, students explored more advanced layout techniques using CSS Grid and Flexbox. They were also introduced to CSS libraries like Bootstrap, which streamlines the process of creating responsive and visually appealing web pages (Bootstrap, 2024).

JavaScript Section: Following HTML and CSS elements, the course dedicated several sessions to JavaScript (JS). During the course, students provided feedback that they were struggling conceptually with JS (compared to HTML and CSS), so we allocated three additional classes to cover it more thoroughly.

Linking JavaScript to HTML: Students learnt how to include JavaScript in their HTML files and how to execute JavaScript code in the browser.

Variables and Data Types: Introduction to declaring variables using let and const, and understanding different data types like strings, numbers, arrays and objects.

Operators: Exploration of arithmetic, assignment, and comparison operators.

Loops and Conditional Statements: Covering for loops, while loops, and if-else statements to control the flow of the program.

React Section: After the JavaScript section, the course moved on to React, aiming to integrate all technologies and create functional applications. React is a JavaScript library for building user interfaces, particularly single-page applications where data dynamically changes over time (React, 2024). The course dedicated several sessions to React fundamentals.

State Management: Students learnt how to manage component states, which is crucial for creating interactive applications. This includes understanding how state influences rendering and how to update states using hooks like useState.

Props: The course covers how to pass data between components using props, ensuring that students understand how to build reusable and modular components.

As students progressed, they were introduced to Firebase, a platform developed by Google, which was primarily used in this course for its real-time database management capabilities (Firebase, 2024). Firebase’s real-time database allows students to store and sync data between users in real-time, providing hands-on experience with dynamic data handling and real-time updates. This exposure helps students understand how to manage and structure databases effectively, handle data synchronisation across multiple clients, and implement CRUD (Create, Read, Update, Delete) operations within their web applications. By integrating Firebase into their projects, students gain practical skills in managing real-time data, which is crucial for developing modern, interactive web applications.

These technologies were selected for their prevalence in web development and their capacity to illustrate core coding concepts in an engaging manner (Domes, 2017). To support students in grasping these technologies, the course encouraged the use of AI tools (particularly OpenAI’s ChatGPT) to assist with syntax challenges. The course adopted a transparent and permissive AI policy: students were encouraged to use such tools freely throughout their work, provided they documented how AI support was used in their code comments or written reflections. Students received guided assistance during their initial development process, with more intensive support provided for complex tasks. The aim of this approach was to ease the cognitive demands of learning to code—a complex skill that involves mastering multiple competencies and is widely regarded as challenging—while also promoting a more supportive and engaging learning environment (Berssanette & de Francisco, 2021). In the final part of the course, students worked in self-selected groups of three students on their own projects, in hopes of bringing their low-fidelity prototypes from Module 1 to life. They were encouraged to use tutors and various resources, including AI, to enhance their solutions. This methodology emphasised the practical aspect of building functional and visually appealing projects, mirroring real-world scenarios where they would have access to a range of tools and resources.

At the end of the semester, students were required to submit a low-fidelity prototype, a functional digital solution, a written report explaining their work from both modules, and a reflective paper on their learning process. The reflective assignment provided valuable insights into their learning experiences and contributed to our understanding, which is discussed further in this paper.

The course comprised 10 ECTS credits, corresponding to approximately 250–300 h of student work (ECTS users’ guide, 2015), and was offered over a 12-week semester. The assessment was made up of two parts. Part one was a group-based project and written report, accounting for 70% of the total grade. Students were permitted to select their own group members. The task required them to develop a digital innovation that provided value to a specific group or purpose (see example in Appendix A). The students were also expected to address and discuss questions related to the design process and the value their digital innovation offered. Additionally, they were required to include a link to their digital solution. This part also included a reflective analysis of their technical literacy. Students were tasked with explaining how the project influenced their understanding and skills related to digital tools and technologies. This analysis was expected to be thoughtful and insightful, detailing their learning process, the challenges encountered, and the strategies used to overcome these challenges.

Part two was an individual oral examination, accounting for 30% of the total grade. This section began with theoretical questions focusing on design processes and rapid prototyping, topics chosen for their relevance to the students’ projects and as a form of random sampling to evaluate theoretical understanding. Students were then required to present their digital solution to the examiner, discuss their application of design principles, and provide a detailed explanation of the code underpinning their solution. The examiner asked questions about theoretical and technical aspects of the project to evaluate each student’s individual contribution and depth of understanding.

2.3. Course Design

Based on the initial lecture and subsequent student feedback, we immediately switched to a flipped classroom model (Akçayır & Akçayır, 2018). In the flipped classroom model, we uploaded videos of the week’s content early in the week, allowing students to seek help or work on exercises related to the current topic during the physical lecture later in the week. This model seemed to work better, though there is still room for improvement in its implementation. In the flipped classroom model, students could pause and rewind the videos, allowing them to learn at their own pace and revisit complex concepts as needed. This approach aligns with the principles of self-directed learning, which emphasise the importance of allowing learners to control the pace and path of their education (Pokhrel et al., 2024). By dedicating class time to exercises and one-on-one assistance, students had more opportunities to clarify doubts and receive personalised feedback, which is critical in mastering new skills like coding (Hattie & Timperley, 2007).

3. Method

This study utilises a qualitative approach to examine learning outcomes and experiences from the course. The data for the study were collected from two forms of student-produced written material generated as part of ordinary course activities. First, all students enrolled in the course (n = 14) submitted group-based exam reflections as part of their final assessment. These reflections, which were capped at 2000 words per group, asked students to discuss how the course had influenced their technical literacy, their understanding of programming concepts, and their ability to work with digital tools and technologies. As collaborative, retrospective accounts of learning, these reflections provided insights into students’ processes, challenges, and interpretations of the course content.

Second, qualitative data were obtained from two open-ended questions included in an anonymous formative midterm survey administered during the semester (see Appendix B). This survey was originally intended to collect feedback for instructional purposes. Seven students submitted individual written comments in response to the open-ended prompts concerning what they found most beneficial in the course and what they believed could be improved. These shorter, individually authored responses complemented the more extensive exam reflections by adding immediate and personal perspectives that were not shaped through group negotiation.

The dataset was analysed using an inductive thematic approach informed by Braun and Clarke’s (2006) principles for thematic analysis. All material was first read to develop familiarity with the content and to identify initial patterns of interest. Two authors then independently conducted open coding of the texts, marking segments that reflected recurring challenges, learning moments, attitudes toward programming, and reflections on the use of tools such as artificial intelligence. Codes were generated inductively to remain grounded in students’ own formulations rather than in predetermined analytical categories. No specialised qualitative analysis software was used; coding was conducted manually using shared documents.

Following the initial coding, the authors met to compare and consolidate their codes. Related codes were grouped into emerging themes through an iterative process, and these themes were reviewed against the dataset to ensure coherence and analytic rigour. The resulting thematic structure captures the central dimensions of students’ experiences and forms the basis of the Section 5.

The use of both group-based reflections and individual midterm responses introduces methodological considerations. Group reflections may reflect collectively negotiated accounts and can obscure individual differences, whereas the midterm responses, while offering personal viewpoints, are brief and vary in depth. For this reason, the analysis treated the two sources as complementary rather than directly comparable. Themes were developed by drawing on patterns evident across the combined dataset, allowing both types of material to contribute to a multifaceted understanding of students’ learning experiences in the course.

4. Ethical Considerations and Use of AI

The research protocol was reviewed by a representative for the Tromsø University School of Business and Economics Research Ethics Committee (TREC) at UiT the Arctic University of Norway. Following this review, the committee leader confirmed that the study did not require formal ethics approval, as it involved the retrospective use of anonymised, non-interventional data generated through ordinary course activities. The study was conducted in accordance with TREC’s ethical guidelines. All students were informed about the purpose of the study, and their participation in the survey and exam assignments was voluntary. Participants’ identities were kept anonymous throughout the research, and all data were treated confidentially. The need for a data management plan was reviewed through the Norwegian Agency for Shared Services in Education and Research (SIKT) and was deemed not necessary to seek approval.

In preparing this article we acknowledge the use of AI-assistance, specifically ChatGPT (OpenAI). ChatGPT was primarily used for language refinement, improving readability, and assisting in translating empirical data from Norwegian to English. The authors retained full control over content selection, interpretation of data, and the conclusions drawn. The authors affirm that the use of AI tools has not compromised the integrity, originality, or scientific rigor of the article.

5. Results and Discussion

To ensure clarity and coherence, the results and discussion are presented together, allowing the findings to be directly linked with their interpretation. Based on our review of the data, we identified four key categories that provide a comprehensive understanding of students’ attitudes, motivation, learning processes, and outcomes. These categories are: (1) Perceived value of coding and technical literacy, (2) Hidden gaps in foundational technical literacy, (3) AI as a cognitive and pedagogical scaffold and (4) emerging technical competence and identity formation. The categories and affiliated data are presented in

Table 1. Themes and illustrative quotes from thematic analysis.

Themes	Student Quotes
Perceived value of coding and technical literacy	[The most beneficial about this course is…] “learning more about technological skills, understanding more relevant skills for work” “It is clear that the demand for people with programming knowledge is increasing, and this knowledge is becoming more relevant and sought after in the labour market.” “The course is practice-oriented, connecting theory to practice”
Hidden gaps in foundational technical literacy	“Despite our high general digital literacy, we started the programming module with low technical literacy in coding. We could easily see opportunities, but it was difficult to assess which of these ideas would actually be feasible.” “The start of the programming module involved simple installations and linking different systems together. Several of us encountered challenges right away, which were solved with help from the lecturer, ChatGPT, and fellow students.” “This feels more difficult than learning Russian.” “There was a lot that was new, both in terms of terminology and concepts, and it was difficult to grasp.”
AI as a cognitive and pedagogical scaffold	“In the work with programming itself, ChatGPT was a tool that was heavily used for troubleshooting and development.” “During the task, we found great benefit in using ChatGPT in several ways. One of the most useful methods was to help us understand error messages and fix them in our code.” “Although it was a very useful tool, we quickly realized that we couldn’t trust what GPT gave us blindly.” “Although we used AI to create components for the code, it was important for us to learn how to code ourselves. To ensure this, we asked GPT to explain all the code it generated.” “ChatGPT was helpful for finding information about components we could use to achieve the desired features and could search through the code to find errors.”
Emerging technical competence and identity formation	“An extremely valuable learning outcome we have achieved is a more extensive understanding of digital business. Digital business understanding involves being able to assess ICT’s role in creating value, driving innovation, and adapting to market and societal changes. It’s about leveraging technology’s potential to enhance business functions and adjust to the changes in the digital landscape.” “Overall, our work on the development of BDKO has been challenging, enriching, and rewarding. The practical approach and content of the course have contributed to an increased understanding and learning outcome and provided a varied study experience. The process has not only strengthened our technical skills but also our ability to understand and navigate the digital world.” “Finally, we can say that working with this module has given us a better understanding of how to use technical terminology and communicate with others who have some knowledge of coding. With this, we can confidently say that working with Module 2 has increased our digital and technical literacy. “We have gained practical experience with various technologies and tools (HTML, CSS, JavaScript, React, Firebase, CSS library: Bootstrap, Netlify) which has broadened our digital competence in multiple areas.” “Although our competence has grown, we acknowledge that the course’s introduction to the subject is foundational, and further in-depth learning is necessary to apply this competence at a professional level.” “The project has expanded our digital competence and provided a solid foundation for further learning and professional development.”

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5.1. Category 1: Perceived Value of Coding and Technical Literacy

This category captures how students perceived the relevance and future value of coding and technical literacy. Across the data, students consistently connected the acquisition of digital skills to employability, innovation work, and academic development. Their perceptions of relevance shaped their motivation, aligning with established research on expectancy–value dynamics in learning. The following observations illustrate how perceived value operated as a key motivational driver in this context.

Students appreciated the connection between theory and practical application. Under the question of what was the most beneficial about the course, one student said, “Learning more about technological skills, understanding more relevant skills for work.” Under the same question of what the student believed was the most beneficial aspect of the course, another student noted: “…the fact that it’s practice-oriented, connecting theory to practice.” This feedback illustrates how the course met students’ expectations by preparing them for both career challenges and the realities of building technical solutions.

Students viewed programming as increasingly valuable for employability, with one student noting “It is clear that the demand for people with programming knowledge is increasing, and this knowledge is becoming more relevant and sought after in the labour market.” This aligns with findings from STEM education research, which underscores the importance of linking learning objectives to real-world applications to enhance student motivation (Schweingruber et al., 2014). From a theoretical perspective, this is consistent with Expectancy-Value Theory (Wigfield & Eccles, 2000), which posits that students are more likely to engage in and invest effort toward tasks they perceive as valuable for achieving their future goals. In this context, students’ enthusiasm for coding reflects both a genuine interest and an awareness of its relevance for their career development. These findings emphasise the critical role of perceived relevance in driving student motivation and engagement. Research shows that connecting course content to students’ goals fosters motivation and deepens engagement (Ambrose et al., 2010)., relying solely on students’ perceptions of relevance may pose challenges. Such perceptions can be influenced by cultural, social, or local factors that may not align with actual skill demands or academic priorities. For example, trends, biases, or rumors—such as labelling a topic as “dumb” or “essential”—can skew evaluations of relevance. Additionally, ideological or societal shifts may lead students to prioritise topics aligned with personal values rather than broader career or societal needs. As Bourdieu (1986) explains, cultural capital and social structures influence educational preferences, leading students to choose fields based on perceived prestige or cultural alignment rather than practical relevance or economic utility.

These findings highlight that perceived relevance is a central motivational mechanism for social science students engaging in technical learning. Our results show that relevance cannot be assumed—it must be made explicit and continually reinforced. This suggests that coding and technical literacy should be embedded within meaningful professional contexts rather than introduced as isolated competencies.

5.2. Category 2: Hidden Gaps in Foundational Technical Literacy

This category reflects the substantial gap between students’ everyday digital fluency and their limited readiness for digital production tasks. The data reveal challenges not only in programming concepts but also in basic operations such as file management, installation processes, and understanding debugging feedback. These patterns highlight the tension between assumed “digital native” proficiency and actual technical literacy.

The students encountered a steep learning curve, which became evident early in the course. Despite the expectation that students would have limited programming skills beforehand, it quickly became apparent that many faced even more challenges than anticipated. This was observed throughout lectures, seminars, and group work, where students struggled with basic technical tasks that are typically assumed to be within their capabilities. One of the first challenges arose when students had difficulty navigating their computers’ file systems and installing necessary software to begin programming. As one student reflected: “The start of the programming module involved simple installations and linking different systems together. Several of us encountered challenges right away, which were solved with help from the lecturer, ChatGPT, and fellow students.” This initial hurdle highlighted the gap in technical literacy, especially for students who had limited experience with the tools and software required for the course. Despite being considered “digital natives” (Yong & Gates, 2014), today’s youth often lack the technical competencies. While they excel as digital consumers, engaging seamlessly with apps and online platforms, their familiarity rarely extends to understanding the underlying mechanics of technology (Ng, 2012; ECDL Foundation, 2014).

While many students were accustomed to using digital tools in other contexts, such as for research or communication, the shift to programming and software development exposed significant gaps in their prior knowledge. As one student noted: “Despite our high general digital literacy, we started the programming module with low technical literacy in coding. We could easily see opportunities, but it was difficult to assess which of these ideas would actually be feasible.” This distinction between general digital fluency and specific technical skills required for coding became a recurring theme throughout the course. These findings align with research showing that digital natives’ proficiency in consumption does not necessarily translate to technical literacy, highlighting a critical gap in their digital skill set (Ng, 2012).

The language of programming and the detailed requirements of the software also posed significant challenges. As students were introduced to new terminologies and code structures, the learning process felt overwhelming at times. One student even remarked during one of the first programming sessions: “This feels more difficult than learning Russian.” This comment, while reflecting the frustration and difficulty of the task, also highlights the steepness of the learning curve and the initial overwhelming nature of the subject. These experiences illustrate the high cognitive load often associated with learning to code, where beginners must manage both the intrinsic complexity of programming concepts and the extraneous load of navigating unfamiliar tools and environments (Sweller, 1988). Reducing extraneous cognitive load through scaffolding and simplified instructions could help students feel less overwhelmed during the initial stages of learning.

Students also struggled with the finer details of the software, including unfamiliar functions, symbols, terminology, and commands, which made it difficult to follow instructions during lectures. As one student described, “There was a lot that was new, both in terms of terminology and concepts, and it was difficult to grasp.” To manage these challenges, students relied heavily on available resources—lectures, recordings, peers, and AI tools such as ChatGPT—for troubleshooting and clarification. These behaviours reflect constructivist perspectives in learning, where understanding develops through active problem-solving (Piaget & Inhelder, 1969), and align with Vygotsky’s (1978) view that learners progress most effectively when supported within their Zone of Proximal Development.

This category addresses a growing challenge in contemporary business education: the persistent misconception that “digital native” students possess advanced or even adequate technical competence (Prensky, 2001; Ng, 2012; ECDL Foundation, 2014). Our findings reinforce research showing that digital consumption skills do not translate into digital production or computational understanding (Bourdieu, 1986; Ng, 2012). This has substantial implications for curriculum design in innovation and management programmes, where coding and digital tools are increasingly included but often without sufficient scaffolding or foundational instruction. Recognising and explicitly addressing these hidden gaps is essential for creating pathways into digital innovation, reducing cognitive overload (Sweller, 1988), and supporting students’ ability to engage meaningfully with emerging technologies. For business schools, this calls for a recalibration of what “baseline” digital literacy truly entails.

5.3. Category 3: AI as a Cognitive and Pedagogical Scaffold

This category captures the role of AI tools—particularly ChatGPT—as cognitive scaffolds that supported students’ navigation of complex programming tasks. Across the data, AI functioned as an auxiliary problem-solving partner, helping students decode error messages, test small components of code, and explore alternative solutions. At the same time, students’ reflections reveal an emerging awareness of the limitations of AI, prompting them to engage critically rather than rely unreflectively on generated outputs. These patterns suggest that AI acted not only as a technical aid but also as a pedagogical mechanism that encouraged metacognitive monitoring and iterative learning. The following observations illustrate how AI contributed to students’ learning processes and shaped their developing technical competencies.

Students frequently used AI for problem-solving and code creation. As one student noted: “In the work with programming itself, ChatGPT was a tool that was heavily used for troubleshooting and development”. This illustrates AI’s central role as a resource for overcoming coding challenges.

One of the most significant advantages of using AI was in debugging and understanding error messages. As one student explained: “During the task, we found great benefit in using ChatGPT in several ways. One of the most useful methods was to help us understand error messages and fix them in our code”. By providing immediate feedback and suggestions, AI streamlined the debugging process, helping students resolve issues more efficiently. This aligns with principles of distributed cognition (Hollan et al., 2000), where cognitive processes are shared between individuals and tools. ChatGPT acted as an external cognitive resource, enabling students to tackle problems that might have otherwise stalled their progress.

However, students quickly learned that critical thinking was essential when working with AI. Despite its usefulness, AI occasionally produced incorrect or incomplete solutions. As one student shared: “Although it was a very useful tool, we quickly realised that we couldn’t trust what GPT gave us blindly”. This experience reflects the development of critical digital literacy (Belshaw, 2012), as students learned to evaluate AI outputs carefully and recognise their limitations.

Importantly, the project emphasised the balance between relying on AI and building fundamental coding skills. While students used AI to generate code components, they also made efforts to understand the outputs rather than relying on them uncritically. One student reflected: “Although we used AI to create components for the code, it was important for us to learn how to code ourselves. To ensure this, we asked GPT to explain all the code it generated”. This approach aligns with constructivist learning theory (Piaget & Inhelder, 1969), which emphasises active engagement with tools to construct understanding. By asking AI to explain its solutions, students deepened their knowledge, ensuring that AI functioned as a learning partner rather than a shortcut.

AI also facilitated the exploration of additional resources, such as components and libraries that could assist in achieving desired functionalities. As one student mentioned: “ChatGPT was helpful for finding information about components we could use to achieve the desired features and could search through the code to find errors”. This ability to quickly identify potential solutions expanded the scope of what students could achieve, highlighting AI’s role in accelerating processes and broadening learning opportunities. However, students noted that poor input often led to ineffective suggestions, underscoring the importance of clear communication with AI tools.

The findings demonstrate that AI tools—especially ChatGPT—can serve as powerful cognitive scaffolds that lower early barriers to entry in programming education. This aligns with established perspectives on distributed cognition (Hollan et al., 2000) and emerging scholarship on AI in higher education (Zawacki-Richter et al., 2019; Holmes et al., 2023). For innovation and business programmes, this indicates that AI can function as a mediator between students’ low initial proficiency and the higher-order analytical skills required in digital innovation work. Notably, students’ critical engagement with AI outputs reflects the development of advanced digital literacy (Belshaw, 2012), suggesting that AI may enhance—not replace—foundational learning when appropriately integrated. This contributes to ongoing debates on how AI can be pedagogically embedded in non-STEM fields to support computational thinking (Wing, 2006) and broaden access to technical literacy.

5.4. Category 4: Emerging Technical Competence and Identity Formation

This category reflects students’ progression from digital consumers to novice digital creators, as well as the emergence of a developing technical identity. The qualitative data indicate that students gained not only discrete technical skills but also a broader conceptual understanding of digital systems and their relevance in innovation and business contexts. Their reflections suggest a growing sense of capability, confidence, and communicative fluency when engaging with technical concepts and tools. These shifts point to the early formation of a technical identity—one rooted in understanding, creating, and discussing digital solutions rather than merely using them. The following findings illustrate how students interpreted their learning trajectories, the competencies they developed, and the professional relevance they attributed to these emerging skills.

Through their work on developing their digital innovation projects, students achieved a considerable increase in both their understanding and digital skills related to digital tools and technologies. As one student reflected, “The process of working on and developing the project has expanded our understanding and enabled us to make simple changes through programming. We have gained greater data competence, meaning we are now able to tell the system what we want it to do”. This shift from passive use of technology to active creation marked a pivotal development in their digital capabilities.

The students’ understanding of digital business also grew significantly during the course. One student described the learning experience as contributing to a more comprehensive digital business understanding: “An extremely valuable learning outcome we have achieved is a more extensive understanding of digital business. Digital business understanding involves being able to assess ICT’s role in creating value, driving innovation, and adapting to market and societal changes. It’s about leveraging technology’s potential to enhance business functions and adjust to the changes in the digital landscape”. This broader understanding of how technology can support business growth and transformation is a key takeaway from the course.

Additionally, the students recognised how programming skills are increasingly in demand in the job market, making them more valuable to employers. As one student put it: “It is clear that the demand for people with programming knowledge is increasing, and this knowledge is becoming more relevant and sought after in the labour market. We, as job seekers, are therefore more valuable and in demand than we were before the course.” This acknowledgment highlights the strong connection between the technical literacy developed in the course and their potential to enhance students’ employability.

Students also noted the practical nature of the course, which not only strengthened their technical skills but also broadened their ability to navigate the digital world. One group shared: “Overall, our work on the development of BDKO has been challenging, enriching, and rewarding. The practical approach and content of the course have contributed to an increased understanding and learning outcome and provided a varied study experience. The process has not only strengthened our technical skills but also our ability to understand and navigate the digital world”. This reflects how the hands-on nature of the course was essential in helping students integrate theoretical knowledge with practical applications.

Furthermore, students now feel more equipped to communicate within technical fields. As one student reflected, “Finally, we can say that working with this module has given us a better understanding of how to use technical terminology and communicate with others who have some knowledge of coding. With this, we can confidently say that working with Module 2 has increased our digital and technical literacy.” The ability to communicate with developers, designers, and other stakeholders is increasingly important in the digital age, and this course has empowered students to engage more effectively in such discussions.

In terms of specific tools and technologies, students gained practical experience with a variety of digital tools that significantly expanded their competencies. “We have gained practical experience with various technologies and tools (HTML, CSS, JavaScript, React, Firebase, CSS library: Bootstrap, Netlify) which has broadened our digital competence in multiple areas.” While the students’ technical skills grew significantly over the course, they acknowledged that further depth in the subject is needed to apply their skills at a professional level. As one student observed: “Although our competence has grown, we acknowledge that the course’s introduction to the subject is foundational, and further in-depth learning is necessary to apply this competence at a professional level.” This recognition of the need for continuous learning aligns with the broader understanding that digital literacy is a dynamic and evolving field.

Ultimately, the project gave the students a solid foundation for continued professional development. As one student reflected: “The project has expanded our digital competence and provided a solid foundation for further learning and professional development.” This is a crucial takeaway, as the ability to adapt and grow in response to the rapid pace of technological change is essential for future success.

The findings suggest that even introductory exposure to coding can initiate shifts in students’ technical identity, moving them from digital consumers to novice digital creators. This contributes to broader discussions about how technical literacy develops within non-technical disciplines (Ritz, 2011; Allen, 2020) and aligns with research on how hands-on, creative coding experiences can foster self-efficacy and deeper digital competence (Nisser et al., 2024). Importantly, students’ growing communicative fluency with technical terminology indicates potential for improved interdisciplinary collaboration—an essential capability in innovation processes (Moorhouse et al., 2024). These findings position technical literacy as not only a cognitive skillset but also an identity resource that enables future innovation professionals to participate more meaningfully in digital development and transformation efforts.

This study contributes to emerging research on technical literacy in higher education by examining a context that has received limited scholarly attention: the integration of coding-based technical literacy within business and innovation programmes. Existing work on digital literacy largely focuses on STEM settings (e.g., Mills et al., 2025; Govender, 2025) and on students’ consumption-oriented digital practices (Ng, 2012), leaving a gap regarding how non-STEM students experience the transition from digital use to digital production. Our findings extend this literature by revealing substantial hidden gaps in foundational technical literacy that challenge prevailing “digital native” assumptions, thereby advancing theoretical understanding of baseline competence in non-technical disciplines. The study also offers one of the first empirical accounts of how AI tools function as cognitive scaffolds in introductory coding for business students, contributing to discussions on distributed cognition and AI-enabled learning. Finally, the identification of emerging technical identity formation among non-STEM learners advances conceptual work on how technical literacy develops as an identity resource, not merely a skills acquisition process. Together, these contributions position coding as a theoretically meaningful pathway for expanding technical literacy and digital capability in business school curricula.

6. Limitations and Further Research

This article is explorative in nature and seeks to develop an initial understanding of how the pedagogical intervention shapes students’ perceptions of their own technical competencies. Given the small number of participants and the absence of a control group or pre–post comparison, the findings cannot support any causal claims. Moreover, the highly contextual design of the course and the reliance on written reflections as the primary data source limit both the generalisability and the replicability of the study. Rather than offering transferable conclusions, our intention is that the article may serve as inspiration for other educators facing similar challenges in integrating technical literacy into non-technical programmes.

Our research did not attempt to demonstrate a clear link between coding and improvements in innovative thinking. A complicating factor is that Module 1 of the course focused on design thinking, and prior studies have shown that students often report an increased sense of innovativeness after completing such courses (Yu et al., 2024). In contrast, this article concentrates on Module 2, which emphasises technical literacy. For this reason, we have intentionally avoided drawing correlations between technical literacy and innovative capacity. Nonetheless, examining the interplay between coding and innovation capability represents a promising avenue for future research.

7. Implications for Practice

The findings from this study offer several concrete recommendations for educators seeking to develop technical literacy among non-STEM students in business and innovation programmes:

Begin with foundational digital operations: Many students lacked basic competencies in file systems, installations and navigating development environments. Early sessions should therefore focus on these core operations before introducing coding concepts.

Use visual, feedback-rich coding environments: Web development offers immediate, tangible outputs that help reduce cognitive load for first-time programmers. This supports motivation and helps students grasp the logic of digital systems more easily.

Integrate AI tools as structured scaffolds: AI systems such as ChatGPT can support debugging, conceptual explanation and problem decomposition, but should be used with guidance to encourage critical evaluation rather than passive dependence.

Adopt a flipped or semi-flipped classroom model: Allowing students to engage with core concepts at their own pace before class time, and using in-person sessions for troubleshooting and applied work, supports mastery of complex material and reduces frustration.

Make professional relevance explicit and continuous: Students’ motivation was strongly tied to perceiving coding as professionally meaningful. Linking technical activities to realistic innovation and business contexts helps sustain engagement.

Provide structured early scaffolding and gradually reduce support: Step-by-step examples, walkthroughs and checklists can help overcome initial learning barriers. As competence grows, support can be tapered to promote independence and identity formation as novice digital creators.

Together, these practices provide a feasible pathway for embedding technical literacy within non-technical programmes and can support social science students in developing confidence and competence in digital production.

8. Conclusions

The findings of this study underscore a persistent gap between students’ everyday digital fluency and the technical competence required to participate meaningfully in digital innovation work. Although the students were often perceived—and perceived themselves—as “born digital,” our analysis shows that their digital skills were primarily consumer-oriented rather than grounded in an understanding of how technologies function. The course sought to bridge this gap by using coding as an entry point to technical literacy, and the data demonstrate that even introductory exposure enabled students to develop more informed, confident, and articulate engagement with digital tools.

By structuring the analysis around four thematic categories, the study contributes to business and innovation education in several ways. First, students’ reflections highlight the centrality of perceived relevance in motivating engagement with challenging technical content. Second, the findings reveal significant hidden gaps in foundational technical literacy, suggesting that assumptions about students’ baseline competence require reconsideration. Third, the integration of AI tools emerged as a meaningful cognitive scaffold, supporting problem-solving while also prompting the development of critical digital literacy. Finally, students demonstrated early signs of technical identity formation, shifting from passive users to novice creators capable of understanding and discussing underlying technological systems.

Taken together, these insights illustrate that technical literacy can be meaningfully developed within social science programmes, even among students with minimal prior experience. For educators, the study offers practical guidance: begin slowly, scaffold foundational skills, integrate AI purposefully, and ensure that coding is embedded within contexts that students see as relevant to their academic and professional futures.

While the study is limited by a small cohort and an exploratory design, it highlights the value and feasibility of including technical literacy as a core component of business school curricula. The findings invite future research on how technical literacy interacts with innovation capability, how learning trajectories unfold across multiple cohorts, and how AI-enabled pedagogies can be optimised for non-STEM learners. Ultimately, fostering technical literacy is not only a matter of skill acquisition; it is a foundation for enabling future managers and innovators to participate with confidence in increasingly digitalised organisational and societal landscapes.