Introduction

While educators see the value of Artificial Intelligence (AI) in boosting student learning and employability, the literature shows uneven awareness of how Generative AI (GAI) supports employability skills from the student perspective (Ayman et al., 2023; Bowles & Kruger, 2021). AI’s ability to analyse data and support individual thinking is transforming education, yet evidence on its impact on employability remains limited (Ayman et al., 2023; Mezhoudi et al., 2021). Nonetheless, technology-driven learning can deepen understanding and build a high-skill communities and society (Fuentes & LaBad, 2025). There is a recognised need to explore how AI is transforming the undergraduate student’s learning experience (Bond, 2024; Bond et al. 2024).

Despite initial caution, educators increasingly recognise GAI applications like ChatGPT as tools for developing self-directed learning, logical reasoning, and soft skills that are key for employability (Ayman et al., 2023; Jung et al., 2024). In the academic disciplines like Business & Management and Humanities, teaching both benefits and limitations of GAI is vital (Ofsted, 2022; Pagani et al., 2023). Technology integration in Higher Education Institutions (HEIs) has improved student engagement with learning (Bond et al. 2020) and employment outcomes, but more clarity is needed on GAI’s specific impact on employability skills (Abbasi et al., 2019; Marin, 2021; Ayman et al., 2023; Mezhoudi et al., 2021). Specifically the paper questions ChatGPT’s effects as a popular application used by students, in terms of impacting their self-directed learning, as self-reflection, and reflexivity (i.e. unspoken thinking) behaviours. Such process it is argued lends to students’ self-evaluation of learning, of learning practice (Balmer,2023; Barton & Ryan,2020) and improved confidence around employability (Hayes,2015; Soros,2013).

To this context, graduate employability defined as the mix of knowledge, skills, and behaviours to secure work is a central goal in UK HEIs with curricula focus on workplace soft skills like communication and teamwork, aligning with employer expectations, even as technology reshapes industry (Cooke et al. 2024). As a result, to address gaps in research, this paper examines how GAI powered virtual reality (VR) simulations, built with ChatGPT driven interviews, support self-directed learning and soft skill development amongst students. This approach allows students to set goals and reflect, with evidence showing increased confidence and workplace relevant behaviours (Boyer et al., 2014; Cooke et al., 2024; Abbasi et al., 2019) and therefore worth examining in the current context.

2. Study conceptual framework

This section begins by outlining the benefits of the use of GAI in virtual reality learning environments. Following this we suggest consideration of learning concepts such as reflection and reflexivity as crucially the drivers of self-directed student learning in virtual learning environments.

2.1 Self-directed learning and GAI initiated virtual reality (VR) experiences

Although GAI tools like ChatGPT have demonstrated clear benefits in higher education, their potential in immersive environments such as virtual reality (VR) experiences is still largely unexplored (Zhai et al,2021). The unique capability of VR, especially when powered by GAI, lies in its ability to build genuine self-directed learning where students set their own goals, manage their learning pace, and actively shape their learning journey. This autonomy is scaffolded by educators, but it is the learner’s sense of ownership and responsibility that drives engagement (Bond, 2020) and builds confidence (Abraham et al.2005; Boyer et al.2014). Immersive technology thus transforms the educational landscape, making learning more purposeful and impactful.

Integrating GAI into learning environments, provides access to vast knowledge and encourages active, independent engagement for students. Learners are prompted to challenge their assumptions, solve problems, and construct new knowledge frameworks (Balmer, 2023). This process not only enhances deep learning but also stimulates creative and critical thinking, self-reflection, and reflexivity ultimately boosting students’ confidence in their abilities (Nanjappa and Grant, 2003; Barton & Ryan, 2008; Soros,2013; Hayes, 2015). The result is a more dynamic and transformative learning experience that prepares students for real-world challenges.

Moreover, when GAI is embedded in VR platforms, learners take centre stage as Avatars, engaging in rich, interactive scenarios that mirror real-life challenges (Mahmoud et al. 2020). ChatGPT powered VR enables students to converse, collaborate, and problem-solve in real time, leveraging sensory VR headsets and controls to navigate and manipulate their environment (Mahmoud et al. 2020,p.481). This setup intensifies self-directed learning, allowing students to personalise their experience and interact meaningfully with both the technology and their peers. While the AI platform orchestrates new experiences, it is the learner’s initiative and engagement that shape outcomes. More advanced simulations involving multiple avatars further amplify these effects, as necessary built up for learning, highlighting the need to critically assess the educational value and extent of self-direction enabled by GAI in VR settings.

In this context, GAI supported VR simulations are powerful tools, curating reasoning, adaptability, and reflective awareness in learners according to (Zhai et al; 2021). Decision making becomes more nuanced as students exercise autonomy, apply knowledge, and adapt behaviours in immersive, interactive settings. The constant interplay between learner and environment, facilitated by the AI platform and tools, drives ongoing self-evaluation and transformation. As learners interact through their avatars, both reflective and reflexive processes are triggered strengthening their capacity for critical self-analysis, in terms of soft skills, and preparing them for the complexities of the modern workplace.

2.2 Student self-reflective & self-reflexive awareness,

Individual self-reflection and its connection to self-reflexivity have been thoroughly examined and are acknowledged as important attributes for graduate employability. We will discuss these concepts further, given their relevance in boosting student confidence (Balmer, 2023; Barton & Ryan, 2020; Shadrikov, 2013; Archer, 2012).

2.2.1 Self-reflection

Reflection is the process of thinking deeply about experiences, which helps individuals gain new knowledge (John, 2009). In learning environments, students use reflection to understand which skills need development and why (Archer, 2012; Grossman, 2008). Several models teach reflection, including Kolb’s experiential learning framework (1984), Gibbs’s iterative model (1988), Schon’s reflection in and on action (1983), and Johns’s reflection during events (2000). Reflection allows students to analyse their thinking and actions, assessing why they apply certain strategies and considering the outcomes. Rooted in introspection, reflection supports practice-based learning by enabling ongoing review, analysis, and evaluation for improvement (Reid, 2000).

Moreover, reflection in education and the workplace is an independent process that helps individuals assess their cognitive skills, actions, and progress toward goals (Rubenstein, 1958; 1995; Gibbs, 1988). The intensity of reflection can trigger emotional responses based on goal achievement and the need for change. Individuals learn by interacting with their environment, as described by constructivism (Piaget, 1970; Papert, 1980) and social cognitivism, where problem-solving relies on social interaction and internal reflection (Gilakjani et al., 2013). Reflection-in-action (Schon, 1983; Bolton, 2014) involves adjusting knowledge and behaviours during problem-solving experiences, creating a framework for evaluating skills and practical application (Heyer, 2015). Effective self-reflection depends on understanding tasks, prior knowledge, emotional investment, feedback, and access to reliable information (Barton & Ryan, 2008). Reflexivity is one higher mental function closely linked to this reflective process.

2.2.2 Self-reflexivity

Reflexivity describes the internal conversations individuals have with themselves, which support behavioural change based on knowledge and social experience (Archer, 2007; Giddens, 1990). It is essential for growth and shaped by emotions that deepen reflective thought (Hibbert, 2019; Cassell et al., 2020). Organisational management literature also links self-reflexivity, reflexive practice, and emotional response (Cunliffe, 2002; Hibbert et al., 2019). Reviews on studies conducted to date on technology based education highlight the need for further insights into the affective effects of such student learning experience (Bond, 2020, Bond, 2019). In education, reflexive learning involves recognising issues, reflecting on abilities, creative and critical thinking, informed decisions, and taking action (Archer, 2012; Grossman, 2008; Barton & Ryan, 2014). Simple exposure to learning rarely equips students for real-world practice, where ongoing re-evaluation and skill revision through reflexivity are crucial. Given its importance to self-directed learning, it is worth considering how GAI applications might influence reflexivity.

There is also recent research that highlights both the potential and limitations of GAI for student learning (Balmer, 2023; Beattie & Hayes, 2020). For example, Balmer (2023) further emphasises ChatGPT's value in fostering reflexive thinking and emotional engagement, enabling learners to critically assess their practice and generate new ideas through dialogic reflexivity. However, Beattie & Hayes (2020) caution that automation may reduce valuable emotional and reflective aspects of learning, particularly when AI replaces educator feedback. While further analysis is needed across contexts, debate continues over whether GAI can truly enhance deep learning, with some calling for more advanced algorithms. Our research question investigates how GAI, specifically ChatGPT, supports reflexive thinking within a graduate interview VR simulation, noting that outcomes may depend on students' confidence with technology as a learning tool (Nicholson & Putwain, 2011).

2.3 Student confidence

Higher student confidence in using technology is linked to stronger performance outcomes (Nicholson & Putwain, 2011). Higher Education (HE) plays a key role in developing workplace ready graduates by building confidence and capability in AI and GAI tools, while embedding employability skills (Meehan & Howells, 2018). Confidence reflects students’ cognitive judgements about their future academic behaviours, tied to academic ability and self-actualisation. It supports behaviours such as self-directed learning, which, alongside clear institutional expectations around study and employability outcomes, contributes to greater academic and career success. GAI can act as a learning enabler, building self-direction, reflection, and reflexivity (Balmer, 2023; Barton & Ryan, 2008). However, research on confidence as an employability competency in HE remains limited, highlighting the importance of examining how GAI may enhance student confidence in technology enabled learning (Jones & Masika, 2020).

2.4 Study Framework

This study explores how GAI, embedded in an immersive VR interview simulation, supports self-directed learning by prompting reflection and reflexive thinking around employability, an under-researched area of technology-enabled learning and examines both its positive and negative effects (RQ1). It also investigates how students’ emotional responses to these reflexive processes shape their confidence (RQ2), offering new insight into how confidence as an employability competency may develop through GAI.

3. Methodology

In partnership with the BodySwaps application, this study explored GAI’s role in supporting self-directed learning within a VR graduate interview simulation. The intervention was embedded in a core second-year BSc (Hons) Business and Management module, assessed through a Portfolio (70%) and Reflective Journal (30%), designed to develop employability skills, professional growth, and career planning. Delivered at a city based HE Business School with a diverse student cohort, the assessments emphasised practical reflection on graduate recruitment processes. Following an open call, 100 students volunteered, with 69 participating. While voluntary sampling can limit representativeness, it enabled an efficient and reliable approach, engaging committed participants (Creswell, 2021). The 30-minute VR simulation included a virtual interview with an AI avatar and a role reversal feature to support reflection. Data were collected via questionnaires and five focus groups (5 participants each, 45–60 minutes), using a mixed-methods design (Creswell, 2021).

Questionnaires combined closed and open items to capture both measurable outcomes and varied student experiences, while focus groups provided deeper insights (Stewart & Shamdasani, 2014). Data addressed: (1) the usefulness of GAI in the simulation; (2) reflection and reflexivity (RQ1); and (3) student confidence (RQ2). Questions were grounded in constructivist and social cognitive frameworks, recognising learning as shaped through interaction with technology (Piaget, 1970; Papert, 1980). Self-directed learning was enabled through the integration of VR hardware and a GAI platform (ChatGPT), supporting interactive, conversational, and problem-solving aspects aligned to learning outcomes. Quantitative data were analysed using descriptive statistics, while qualitative data underwent coded thematic analysis using a two-stage process to identify themes and patterns (Saldana, 2013). Ethical approval ensured informed consent, voluntary participation, anonymity, and secure data handling.

4. Findings

Given the limited research around student’s perspectives on technology-initiated learning, we firstly address the barriers initiated by the ChatGPT functionality. Through RQ1, findings address the extent of self-reflection and reflexive-thinking as attributes of self-directed learning, that were enabled around workplace-relevant employability attributes. We then address how such self-directed learning concepts (Mageira et al.2022) impacted student confidence (RQ2).

4.1 Student’s experiences of the AI-initiated VR simulation

Students’ experiences of the simulation, in their interactions with the avatar interviewer was assessed through Q4 (a. to f.) within the questionnaire (Table 1- see below) and through focus groups (FGs) (see Appendices Table.4). Responses around the simulation’s effectiveness was analysed based on questions that helped elicit Chat-GPT’s contribution in generating realistic and/or relatable interview scenarios, and/or (non)predictable challenging interview questions. Given AI’s drawbacks (e.g. automation of learning - Beattie & Hayes,2020, disconnect from emotionality/human aspects -Zhai et al.2021; Greenfield,2009), table 1, reveals some positive findings.

Table 1: Questionnaire: student experience of the effectiveness of the avatar (interviewer).

14% of students strongly agreed (SA), 37% agreed that the simulation emulated a realistic interview, although the lower response ratings are questionable. What factors underpinned the lower raters underpin? Here FG2 revealed concerns about the automated (Beattie & Hayes,2020) aspects of interview questions, the lack of social-connection: “I think that it is as close as about 87- 89% as the questions were delivered fairly sequentially” “It was unlike a real interview as laughter and conversation was lacking”. Interviewee 3, (FG3), similarly highlighted: “in a real interview you are walking into a room and interacting with people, sort of shuffling about, whereas here you’re just sort of sat there”. Aesthetically the simulation was lacking, though students also felt challenged around unexpected interview questions (SA – 21%; A- 33%). 30% students agreed, and 5% SA around the predictability of interview questions, 26% remained neutral, while 22% SA and 14 remained neutral about challenging questions. Students highlighted the simulations’ usefulness as a training tool only, during FGs: (FG4): “It’s a useful tool to practice the questions as I did not feel the pressure”). To emulate, a more realistic experience, these varied points are justification for the need for a re-design of the simulation, and this was a consistent theme in the study.

4.2 Reflection & reflexivity that student’s access during the AI-initiated VR (RQ1)

Despite the above backdrop, students were quite positive of the reflection and reflexivity, they faced (Young,2018:40).

4.2.1 Reflection and reflective awareness around employability concepts-(RQ1)

Here students reflected on overall performance; the skills applied during the interview-simulation including during the simulation’s feedback when students swapped places with an interviewer avatar to observe their performance. The simulation enabled reflections on developing employability/softs skills, (Q6 – questionnaire), including social interaction (26%), critical thinking (22%), problem-solving (12%), thinking (22%), listening (13%), judgement (3%), decision-making (7%), verbal/conversational (23%), emotional awareness (13%), resilience (1%), adaptability (7%), and commercial awareness (9%) (Table 2 below).

Table 2: Questionnaire responses: student reflection

The feedback facility produced a constructive learning experience (“it felt constructive as you knew that at the end that you could get feedback”) (see also Table 4 in the Appendix). Feedback enhanced students’ reflections on employability/interviewing skills (“though the good thing here is that when you swap you can see how you’ve been moving your hands or if you’ve been slouching” – FG3; “I didn't look at the interviewer when responding, and I got feedback that my eye contact was not good, so the feedback was precise.' FG2). Others used the simulation to reflect on overall performance (“I had to and could think about what I could have done better during and throughout the experience”).

Table 2 highlights the soft skills that fewer students reflected on including: resilience, judgment, decision-making, and adaptability. Also, given that reflection is a concept that is enabled in VR learning environments, only 1% of students highlighted that the experience enabled reflection of employability skills, revealing a lack of awareness/understanding of reflection. During FGs however, students revealed reflecting critically on various interviewing skills (e.g. thinking skills – “thinking on one’s feet”, I was able to think creatively”; adaptability – I learnt about responding to questions in a timely manner, which I got better at as the interview progressed”; “I was more aware of answering curve-ball questions effectively; conversational skills – “ I think that interacting with the avatar lends to variation in conversation style; problem-solving and attentiveness towards problem-solving”). So here the findings suggest that students reflected also on their performance around applying softs skills, around the interview questions initiated by the avatar/generative-AI system, as an advantageous feature.

In table 3 (see below), questions (Qs 4g to 4k) explore the extent of student reflection initiated by the simulation around the module’s LOs. Table 3 reveals higher response rates around students’ agreeing around the reflections that took place this time in the application, practice and development of graduate interviewing skills. 21% SA and 38% were able to reflect on what not to do during a graduate interview; on developing interviewing skills (19% - SA; 38% - A); and on developing employability attributes (14% - SA; 37% - A); on testing knowledge learnt on other modules (13% -SA; 35%-A) and on reflecting upon asking questions around job-related knowledge, skill and behaviours (11%- SA; 30%-A). Such insight reveals that the AI-initiated VR-simulation did in fact facilitate in students’ reflection, as a learning attribute. 13,17 and 20% of students opted neutral towards the previous three questions, suggesting either an undecidedness/uncertainty around these questions. 20% of students revealed an undecidedness towards reflecting on asking employer-related questions, as a critical practice of employer interviews while 8% disagreed. Given that the purpose of the VR experience, was to also ensure students’ self-directed learning, through enhancing awareness of reflection, its practice around the module LOs, this latter finding reveals a broader unfamiliarity of reflection as a learning approach.

Table 3: Questionnaire responses: student confidence

4.2.2 Reflexivity-RQ1

Evidence of reflexive practice in student’s reflections of the simulation, suggested that students engaged with their inner voice around their anxieties in answering interview questions around demonstrating employability and interview skills: “for me there were questions where I really struggled to come up with the answers. I’ve learnt that I need to practice more and thought during the interview that in the experience I worry too much about what I’m going to worry about how I thought I should answer a question if that makes sense?” (FG1). Clear here is the worry and stress but also the reflexive thought process around worrying during the interview and further also a resolution: “… thought during the interview that I worry too much about what I’m going to worry about and how I should answer a question”. Reflections and reflexive thoughts also mirrored intentions around behavioural change: “for me it was that I speak a little too fast and I’m thinking that I’m nervous. So I learnt that in the first question as I was nervous, I jittered through I and so the feedback in the simulation then tells you the speed with which you should speak and how you should slow it down. That is what I worked on in my second go” (FG3). Demonstrated here is an inner-voice around being “nervous”, and it is this inner voice, that triggers the realisation to change behaviour, a process enabled during the simulation’s feedback capability. This example also demonstrates independent reasoning, decisions to change behaviour, as progressive learning opportunities. Simulations also initiated thinking reflexively about performance: (“For me it was not really about the questions that were being asked, but about thinking about whether I was able to respond effectively to the questions” (FG4). Students’ reflexive-thinking further enabled the realisation to adjust interview learning/performance behaviours (“For me I became more aware of how unprepared I am and thought to myself during the experience the questions I would need to prepare for during an actual interview”, FG3). Overall, the reflexivity during the simulation raised awareness of improving interview practice, as a useful finding.

Student’s reflexive-thinking extended beyond the interview around future-focused improvements/changes in behaviour around career (Bridgestock et al.2019) (FG5: “so one of the questions was about my contribution towards the company what I would like to do if employed by an organisations…and it got me thinking during the interview…I thought what I would like to do”). Our findings here reveal links between student’s reflexivity, emotionality, and behavioural change, as intertwined concepts, that improved student confidence.

4.3 Linking reflection, reflexive-thinking with student confidence (RQ2)

Due to the self-directed learning concepts highlighted previously, overall, the VR experience was effective in raising student confidence (see Table 4 and Fig.1 in appendices).

Most students agreed that interview confidence improved as the simulation progressed, though again caution is needed around interpreting the neutral results, on where students are uncertain, or attribute low importance to such concept. FGs helped contextualise the confidence students experienced, also when engaging with reflection or reflexivity (Balmer,2023). Confidence grew as the “experience progressed”, (“I became more confident as the interview experience progressed. I reflected maybe learnt more by myself, and I said to myself I can do this, of course I can do this. I think that’s how my confidence grew and I felt less stress, I felt good.”FG4). This student draws links between gaining confidence and independent reflection (“I reflected maybe learnt more by myself”), and reflexivity (I said to myself I can do this, of course I can do this myself”). Belief in interviewing ability was also important (“I can do this”), as was student’s emotionality (“I felt less stress more confident as the experience progressed and I felt good”). The reflexivity demonstrates that the simulation enabled students to independently better deal with negativity, as the experience progressed (Hayes,2015; Soros,2013) (“I said to myself I can do this, of course I can do this”), in turn initiating positive emotions and raised confidence. Given these and the findings in table 4 we next, we address the discussions and the study’s implications.

5. Discussion & study implications

Overall, students reported mixed views on the effectiveness of the ChatGPT simulation highlighting issues of realism, relatability, and predictability. Unrealistic aesthetics and repetitive interview questions suggest a need for better instructional design, including clearer pre-briefing and adaptive AI capable of varying cognitive complexity to meet diverse learning needs (Cooke et al., 2024; Jopp, 2019). For RQ1, the simulation prompted reflection on interview and employability skills, though awareness was uneven. Key employer-valued skills (e.g. resilience 1%, adaptability 7%, commercial awareness 9%) were under recognised, and 20% of students were unsure about reflecting on asking essential employer questions. This highlights the need for structured pre-training in reflective practice and interview techniques within ChatGPT enabled simulations.

On the other hand, students engaged strongly with their reflexive voice (Cunliffe, 2002; Hibbert et al., 2019), using it to manage anxiety and develop self-initiated coping strategies. Reflexivity functioned as both a psychological support and a motivator helping students identify skill gaps, prepare for future scenarios, and overcome barriers. These insights point to the need for preparatory and wellbeing support alongside such technologies. Reflexive thinking also drove self-motivation and confidence, particularly for students who initially found the experience daunting (Young, 2018). Confidence increased through repeated interaction with the ChatGPT simulation, suggesting familiarity is key. Crucially, connecting emotional responses to reflexive thinking (Hayes, 2015; Soros, 2013) enabled students to form future-focused employability insights (Hibbert et al., 2019).

These findings position reflection and reflexivity as essential to self-directed learning, student engagement (Bond, 2019, 2020) and confidence building in GAI environments, with clear implications for embedding these practices into simulation design and pedagogy.

6. Conclusion

Despite challenges with ChatGPT’s visual realism and conversational quality, this paper makes a key contribution through its self-directed learning framework (Fig.1) linking reflection, reflexivity, emotional responses, and increased student confidence. In doing so it speaks to the literature calling for insights on student engagement factors of educational experiences initiated through technology (Bond, 2020, 2019). While limitations included automation effects (e.g. replication - Beattie & Hayes, 2020), reduced realism, and constrained cognitive challenge, more technologically familiar students reported fewer issues. Consistent with existing literature, immersive AI-enabled VR environments supported interaction-rich, deep learning enhancing self-direction, self-evaluation, and reflective reasoning as core employability skills. The findings emphasise the need for greater support in developing reflection as a critical component of technology enabled learning. It also highlights reflexivity (Balmer, 2023; Barton & Ryan, 2008) and emotional engagement (Bond, 2020, Bond, 2019) as key drivers of self-directed learning. Given the enhanced emotional engagement, future research perhaps can explore how reflexive thinking models and framework may be integrated as part of learning experiences shaped by technology (Bond, 2020, 2019).

Limitations include the focus on second-year Business and Management students within one UK institution and the use of voluntary sampling. While the sample size was moderate, the study does not explore variation across demographic factors such as socio-economic background, gender, or age, presenting a direction for future research. This is increasingly important as GAI becomes embedded in graduate recruitment (Jung et al., 2024) and employability focused higher education learning (Bobula, 2024; Pagani et al., 2023; Jones & Masika, 2020).

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