# Artificial Intelligence in Professional and Vocational Training

Solange Ciavaldini-Cartaut, Jean-François Métral, Paul Olry, Dominique Guidoni-Stoltz, Charles-Antoine Gagneur

► To cite this version:

Solange Ciavaldini-Cartaut, Jean-François Métral, Paul Olry, Dominique Guidoni-Stoltz, Charles-Antoine Gagneur. Artificial Intelligence in Professional and Vocational Training. Creative Applications of Artificial Intelligence in Education, Springer Nature Switzerland, pp.145-155, 2024, Palgrave Studies in Creativity and Culture, 978-3-031-55271-7. 10.1007/978-3-031-55272-4_11. hal-04616019

HAL Id: hal-04616019

https://institut-agro-dijon.hal.science/hal-04616019

Submitted on 18 Jun 2024

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# Artificial Intelligence in Professional and Vocational Training

Solange Ciavaldini-Cartaut, Jean-François Métral, Paul Olry, Dominique Guidoni-Stoltz, Charles-Antoine Gagneur

► To cite this version:

Solange Ciavaldini-Cartaut, Jean-François Métral, Paul Olry, Dominique Guidoni-Stoltz, Charles-Antoine Gagneur. Artificial Intelligence in Professional and Vocational Training. Alex Urmeneta, Mar-garida Romero. Creative Applications of Artificial Intelligence in Education, Springer Nature Switzer-land, pp.145-155, 2024, Palgrave Studies in Creativity and Culture, 978-3-031-55271-7. 10.1007/978-3-031-55272-4_11. hal-04616019

HAL Id: hal-04616019

https://institut-agro-dijon.hal.science/hal-04616019v1

Submitted on 18 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

11

# Artificial Intelligence in Professional and Vocational Training

Solange Ciavaldini-Cartaut, Jean-François Métral, Paul Olry, Dominique Guidoni-Stoltz, and Charles-Antoine Gagneur

**Abstract** This chapter explores challenges linked to AI integration in professional training, emphasising the need to question the learning analytics generated during activities involving humans or living entities. The intersection of initial and ongoing training poses a design challenge: making AI tools acceptable for both training and the workplace. This chapter critically examines the nature and quality of the initial data and learning analytics using educational data mining through three case studies of adaptive learning environments. The first case study addresses

S. Ciavaldini-Cartaut (✉)

Université Côte d'Azur, Nice, France

e-mail: Solange.CARTAUT@univ-cotedazur.fr

J.-F. Métral · P. Olry · D. Guidoni-Stoltz · C.-A. Gagneur

Institut Agro Dijon, Dijon, France

e-mail: jean-francois.metral@agrosupdijon.fr

P. Olry

e-mail: paul.olry@agrosupdijon.fr

D. Guidoni-Stoltz

e-mail: dominique.guidoni-stoltz@agrosupdijon.fr
11

# Artificial Intelligence in Professional and Vocational Training

Solange Ciavaldini-Cartaut, Jean-François Métral,
Paul Olry, Dominique Guidoni-Stoltz,
and Charles-Antoine Gagneur

**Abstract** This chapter explores challenges linked to AI integration in professional training, emphasising the need to question the learning analytics generated during activities involving humans or living entities. The intersection of initial and ongoing training poses a design challenge: making AI tools acceptable for both training and the workplace. This chapter critically examines the nature and quality of the initial data and learning analytics using educational data mining through three case studies of adaptive learning environments. The first case study addresses

S. Ciavaldini-Cartaut (✉)
Université Côte d'Azur, Nice, France

e-mail: Solange.CARTAUT@univ-cotedazur.fr

J.-F. Métral · P. Olry · D. Guidoni-Stoltz · C.-A. Gagneur
Institut Agro Dijon, Dijon, France

e-mail: jean-francois.metral@agrosupdijon.fr

P. Olry

e-mail: paul.olry@agrosupdijon.fr

D. Guidoni-Stoltz

e-mail: dominique guidoni-stoltz@agrosupdijon.fr
the challenges of modelling Comté cheese manufacturing. The second case study describes Silva Numerica, a digital forest simulator, exploring how AI, as a learning tool, can contribute to realistic modelling while addressing didactic obstacles. The third case delves into AI's role in automotive mechanics training, emphasising the need for visibility in cognitive inference processes. The chapter concludes by addressing data reliability concerns in AI systems and proposing education and training strategies to overcome such challenges.

**Keywords** Professional training · Vocational training · Learning analytics · Artificial intelligence · Digital learning environment

**Introduction**

In order to develop an AI system, it is essential to possess dependable data that can be utilised to facilitate the process of teaching and learning. In vocational training, this type of data is distinguished by its emphasis on practical knowledge and the use of informal, deliberative, and adaptable regulations in the workplace, with a focus on productivity. A second challenge in developing an AI in professional training lies in maintaining the educational value of real-life experiences, which encompass a wide range of situations, various approaches, and different outcomes. This must be done while simultaneously taking into account the applicability of real-world work scenarios for educational objectives (Ciavaldini-Cartaut et al., 2022).

The challenges of using AI-powered smart tutors present several obstacles: (i) providing feedback for human interactions over extended periods, and (ii) the absence of detailed descriptions regarding the actual work. This chapter delves into the criticisms and questions surrounding AI in professional training, specifically in the context of alternating between formal education and workplace training. The analyses are grounded in the cultural, anthropocentric, and socio-technical paradigm of technology use (Albero, 2019). AI can be considered as instruments for operations, processes, and access to information supporting the teaching-learning process (Folcher & Rabardel, 2004). While AI can enhance cooperation and support learning analytics, challenges arise in
the challenges of modelling Comté cheese manufacturing. The second case study describes Silva Numerica, a digital forest simulator, exploring how AI, as a learning tool, can contribute to realistic modelling while addressing didactic obstacles. The third case delves into AI's role in automotive mechanics training, emphasising the need for visibility in cognitive inference processes. The chapter concludes by addressing data reliability concerns in AI systems and proposing education and training strategies to overcome such challenges.

**Keywords** Professional training · Vocational training · Learning analytics · Artificial intelligence · Digital learning environment

**Introduction**

In order to develop an AI system, it is essential to possess dependable data that can be utilised to facilitate the process of teaching and learning. In vocational training, this type of data is distinguished by its emphasis on practical knowledge and the use of informal, deliberative, and adaptable regulations in the workplace, with a focus on productivity. A second challenge in developing an AI in professional training lies in maintaining the educational value of real-life experiences, which encompass a wide range of situations, various approaches, and different outcomes. This must be done while simultaneously taking into account the applicability of real-world work scenarios for educational objectives (Ciavaldini-Cartaut et al., 2022).

The challenges of using AI-powered smart tutors present several obstacles: (i) providing feedback for human interactions over extended periods, and (ii) the absence of detailed descriptions regarding the actual work. This chapter delves into the criticisms and questions surrounding AI in professional training, specifically in the context of alternating between formal education and workplace training. The analyses are grounded in the cultural, anthropocentric, and socio-technical paradigm of technology use (Albero, 2019). AI can be considered as instruments for operations, processes, and access to information supporting the teaching-learning process (Folcher & Rabardel, 2004). While AI can enhance cooperation and support learning analytics, challenges arise in
applying it to complex, 'with and for the living' activities where observ-
able behaviours fall short in capturing the intricate cognitive, adaptive,
and creative processes required in work situations. Learning analytics, in
this context, proves limited in comprehending these nuanced processes
(Albero, 2019).

This chapter critically examines the nature and quality of the initial
data and learning analytics using educational data mining. Empirical
illustrations include the adaptive reasoning challenges in Comté cheese
manufacturing (Chrétien et al., 2020) and the e-Fran research, Silva
Numerica (https://silvanumerica.net/), focusing on a forest management
learning environment (Chiron, 2018; Guidoni-Stoltz, 2019, 2020). The
latter explores how AI, as a learning tool, can contribute to realistic
modelling while addressing didactic obstacles. The third illustration
delves into AI's role in automotive mechanics training (Gagneur &
Vassout, 2019), emphasising the need for visibility in cognitive inference
processes.

The Use of Professional Analytics
in Designing AI Tools

In both social and educational contexts, discussions on professional skills
and tasks abound. Yet, complexities arise when considering the actual
work done within companies, primarily due to the proprietary nature of
productive work and the inherent difficulties of documenting intricate
work processes. Furthermore, very little is known about the specific and
often diverse processes required to complete a job, particularly when that
job requires collaborating with other humans in a dynamic environment.

This chapter addresses the challenge of identifying relevant data points
for evaluating learning in professional environments when the work
done includes a mix of discretionary and prescribed tasks. Companies
often rely on procedures, but the associated knowledge is frequently
disconnected from the actual complexities of real workplace activities.
Furthermore, while simulations can provide more realistic situations
from which to study, their effectiveness in regard to teaching or learning
is well-documented within the field of professional didactics.
applying it to complex, 'with and for the living' activities where observ-
able behaviours fall short in capturing the intricate cognitive, adaptive,
and creative processes required in work situations. Learning analytics, in
this context, proves limited in comprehending these nuanced processes
(Albero, 2019).

This chapter critically examines the nature and quality of the initial
data and learning analytics using educational data mining. Empirical
illustrations include the adaptive reasoning challenges in Comté cheese
manufacturing (Chrétien et al., 2020) and the e-Fran research, Silva
Numerica (https://silvanumerica.net/), focusing on a forest management
learning environment (Chiron, 2018; Guidoni-Stoltz, 2019, 2020). The
latter explores how AI, as a learning tool, can contribute to realistic
modelling while addressing didactic obstacles. The third illustration
delves into AI's role in automotive mechanics training (Gagneur &
Vassout, 2019), emphasising the need for visibility in cognitive inference
processes.

The Use of Professional Analytics
in Designing AI Tools

In both social and educational contexts, discussions on professional skills
and tasks abound. Yet, complexities arise when considering the actual
work done within companies, primarily due to the proprietary nature of
productive work and the inherent difficulties of documenting intricate
work processes. Furthermore, very little is known about the specific and
often diverse processes required to complete a job, particularly when that
job requires collaborating with other humans in a dynamic environment.

This chapter addresses the challenge of identifying relevant data points
for evaluating learning in professional environments when the work
done includes a mix of discretionary and prescribed tasks. Companies
often rely on procedures, but the associated knowledge is frequently
disconnected from the actual complexities of real workplace activities.
Furthermore, while simulations can provide more realistic situations
from which to study, their effectiveness in regard to teaching or learning
is well-documented within the field of professional didactics.
In the realm of school education, obtaining tangible data seems more straightforward, with established scientific and didactic knowledge. However, the world of work presents challenges where efficiency is correlated with immediate or long-term results, making judgments about individuals' work and the data associated with that work, 'awkward to say' (Dujarier, 2010). Vocational training stands at the crossroads of using tangible learning analytics (Knight & Buckingham, 2017) and data faithful to work situations (Chiron, 2018). The chapter concludes by addressing the challenges of developing AI tools or implementing machine learning algorithms for work situations, where the initial data may lack the precision necessary to foster effective learning.

Exploring AI in Vocational Training: The Case of French Comté Cheese

The production of French Comté cheese (Chrétien et al., 2020) is well-documented and offers insights into the challenges and potential of AI in vocational training. Despite extensive data on standardised procedures, including factors like cow types, weather, and soil conditions, a crucial anthropocentric perspective reveals a gap in understanding essential variables. These variables, which influence a cheesemaker's decision-making for distinct flavours, remain elusive. As such, while procedural data could be used to train AI tools for this type of vocational environment, the inclination towards industrialised processes over contextual reasoning presents a socio-technical problem: manufacturers and management tend to prioritise procedures, not reasoning, when designing their professional training. This bias raises questions regarding data selection and the role of humans in AI design, distinct from the context of school education.

The case of Comté cheese production highlights workplace learning, prompting inquiry into how AI could impact learning in professional settings. Littlejohn (2017) notes challenges in modelling complex, dynamic environments due to inadequate information sensors. In Comté cheese manufacturing, perceptual and gestural dimensions, crucial for the cheesemaker's decision-making, lack suitable sensors for AI interpretation. The political and technical intricacies of collecting tangible
In the realm of school education, obtaining tangible data seems more straightforward, with established scientific and didactic knowledge. However, the world of work presents challenges where efficiency is correlated with immediate or long-term results, making judgments about individuals' work and the data associated with that work, 'awkward to say' (Dujarier, 2010). Vocational training stands at the crossroads of using tangible learning analytics (Knight & Buckingham, 2017) and data faithful to work situations (Chiron, 2018). The chapter concludes by addressing the challenges of developing AI tools or implementing machine learning algorithms for work situations, where the initial data may lack the precision necessary to foster effective learning.

Exploring AI in Vocational Training: The Case of French Comté Cheese

The production of French Comté cheese (Chrétien et al., 2020) is well-documented and offers insights into the challenges and potential of AI in vocational training. Despite extensive data on standardised procedures, including factors like cow types, weather, and soil conditions, a crucial anthropocentric perspective reveals a gap in understanding essential variables. These variables, which influence a cheesemaker's decision-making for distinct flavours, remain elusive. As such, while procedural data could be used to train AI tools for this type of vocational environment, the inclination towards industrialised processes over contextual reasoning presents a socio-technical problem: manufacturers and management tend to prioritise procedures, not reasoning, when designing their professional training. This bias raises questions regarding data selection and the role of humans in AI design, distinct from the context of school education.

The case of Comté cheese production highlights workplace learning, prompting inquiry into how AI could impact learning in professional settings. Littlejohn (2017) notes challenges in modelling complex, dynamic environments due to inadequate information sensors. In Comté cheese manufacturing, perceptual and gestural dimensions, crucial for the cheesemaker's decision-making, lack suitable sensors for AI interpretation. The political and technical intricacies of collecting tangible
analytics pose questions about industry willingness, standards, and
components necessary for documenting an AI system. While high-risk
industries have addressed these challenges, AI's impact in vocational
training extends to other sectors, necessitating a broader exploration of
its potential influence.

AI Support in Simulated Environments: The
Silva Numerica Project

One potential application of AI in vocational training is its role as a
learning partner. This involves utilising AI as a support tool to enhance
various aspects of professional learning. For instance, AI could help
document reliable data for designing simulations that replicate work situ-
ations, especially those challenging for future professionals to access. It
could also contribute to improving the handling of critical situations,
be it in product development, manufacturing processes, or collaborating
with people. Consider the risk-laden scenarios of taking-off and landing
a commercial aircraft at a congested airport during peak travel season. An
AI tool could document the processes necessary for a pilot to complete
these tasks, however, it would be unable to articulate the subtle or invis-
ible learnings, acquired through repetition, over extended periods of
time. AI would face a similar challenge in documenting the processes
and actions related to managing a forest within a complex ecosystem
that evolves over long periods of time, absent human intervention. The
Silva Numerica project (Guidoni-Stoltz, 2019, 2020) has meticulously
gathered data documenting forest management work with the goal of
designing a digital learning environment that can capture the intricacies
of this field. Despite their efforts, the virtual simulator they developed for
educational purposes encounters significant limitations. First, the natural
developmental processes it seeks to replicate are inherently complex,
lacking a ready-made global model for training or a comprehensive
database. This complexity is further compounded by its dependence on
contextual factors such as climate, commercial outlets, and specific silvi-
culture goals and practices (Mayen & Lainé, 2014). Similar challenges
are highlighted in the study by Chrétien et al. (2020) concerning cheese
analytics pose questions about industry willingness, standards, and
components necessary for documenting an AI system. While high-risk
industries have addressed these challenges, AI's impact in vocational
training extends to other sectors, necessitating a broader exploration of
its potential influence.

AI Support in Simulated Environments: The
Silva Numerica Project

One potential application of AI in vocational training is its role as a
learning partner. This involves utilising AI as a support tool to enhance
various aspects of professional learning. For instance, AI could help
document reliable data for designing simulations that replicate work situ-
ations, especially those challenging for future professionals to access. It
could also contribute to improving the handling of critical situations,
be it in product development, manufacturing processes, or collaborating
with people. Consider the risk-laden scenarios of taking-off and landing
a commercial aircraft at a congested airport during peak travel season. An
AI tool could document the processes necessary for a pilot to complete
these tasks, however, it would be unable to articulate the subtle or invis-
ible learnings, acquired through repetition, over extended periods of
time. AI would face a similar challenge in documenting the processes
and actions related to managing a forest within a complex ecosystem
that evolves over long periods of time, absent human intervention. The
Silva Numerica project (Guidoni-Stoltz, 2019, 2020) has meticulously
gathered data documenting forest management work with the goal of
designing a digital learning environment that can capture the intricacies
of this field. Despite their efforts, the virtual simulator they developed for
educational purposes encounters significant limitations. First, the natural
developmental processes it seeks to replicate are inherently complex,
lacking a ready-made global model for training or a comprehensive
database. This complexity is further compounded by its dependence on
contextual factors such as climate, commercial outlets, and specific silvi-
culture goals and practices (Mayen & Lainé, 2014). Similar challenges
are highlighted in the study by Chrétien et al. (2020) concerning cheese
making. While the manufacturing process, well-documented with precise
and reliable data, lends itself to potential AI development, the ways in
which these processes are implemented remain implicit. Therefore, the
lack of thorough data becomes apparent when considering the variability
and diversity of real-world work situations. For these same reasons, the
design and use of AI-powered simulators also become a challenge. Some
studies, including recent work by didacticians like Vadcard (2013, 2019),
underscore the nuanced nature of simulation effectiveness. They argue
that achieving realism in simulating complex and dynamic environments
doesn't guarantee training effectiveness. In fact, excessive realism might
be counterproductive, as modifying reality for pedagogical purposes is
often a prerequisite for transforming situations into meaningful learning
experiences. For example, Silva Numerica's forest simulation, while rela-
tively realistic based on regular high forest silviculture of sessile oaks,
introduces a simplified representation of trees as 'lollipop sticks'. This
departure from realism serves as a semiotic resource for learning, aiding
in tasks like selecting trees based on their ability to contribute to forestry
development.

This raises the question: to what extent can AI contribute to modelling
a training process based on databases of work tasks, outside of practice
situations? Furthermore, how can AI facilitate modelling and simulations
when dealing with poorly documented phenomena and processes?

The Challenges of Utilising AI and Intelligent
Tutors in Professional Learning

A pivotal inquiry in our exploration pertains to the capacity of AI to
furnish pertinent feedback for learning. As elucidated by Hwang et al.
(2020), the concept of AI as an intelligent tutor is prevalent in the
literature. The crux of AI's use in professional training resides in its
ability to scrutinise learner activity and generate meaningful feedback.
This poses a formidable challenge, as it hinges on the expectation that
AI technologies can dissect a learner's actions on the machine, explicate
the validity or invalidity of their achievements, and assess the associated
learning quality. In professional domains like forest management, where
making. While the manufacturing process, well-documented with precise
and reliable data, lends itself to potential AI development, the ways in
which these processes are implemented remain implicit. Therefore, the
lack of thorough data becomes apparent when considering the variability
and diversity of real-world work situations. For these same reasons, the
design and use of AI-powered simulators also become a challenge. Some
studies, including recent work by didacticians like Vadcard (2013, 2019),
underscore the nuanced nature of simulation effectiveness. They argue
that achieving realism in simulating complex and dynamic environments
doesn't guarantee training effectiveness. In fact, excessive realism might
be counterproductive, as modifying reality for pedagogical purposes is
often a prerequisite for transforming situations into meaningful learning
experiences. For example, Silva Numerica's forest simulation, while rela-
tively realistic based on regular high forest silviculture of sessile oaks,
introduces a simplified representation of trees as 'lollipop sticks'. This
departure from realism serves as a semiotic resource for learning, aiding
in tasks like selecting trees based on their ability to contribute to forestry
development.

This raises the question: to what extent can AI contribute to modelling
a training process based on databases of work tasks, outside of practice
situations? Furthermore, how can AI facilitate modelling and simulations
when dealing with poorly documented phenomena and processes?

The Challenges of Utilising AI and Intelligent
Tutors in Professional Learning

A pivotal inquiry in our exploration pertains to the capacity of AI to
furnish pertinent feedback for learning. As elucidated by Hwang et al.
(2020), the concept of AI as an intelligent tutor is prevalent in the
literature. The crux of AI's use in professional training resides in its
ability to scrutinise learner activity and generate meaningful feedback.
This poses a formidable challenge, as it hinges on the expectation that
AI technologies can dissect a learner's actions on the machine, explicate
the validity or invalidity of their achievements, and assess the associated
learning quality. In professional domains like forest management, where
diverse and context-dependent outcomes may be valid, the interpretative
challenge for AI intensifies.

AI's interpretation must acknowledge that conformity with a result doesn't necessarily imply the learner's comprehension of underlying strategies, procedures, or professional knowledge. Moreover, AI's feedback cannot be solely aligned with good practice or standard reasoning, as various other legitimate reasonings may lead to comparable outcomes. Hence, establishing indicators that can evaluate a learner's acquired knowledge or identify difficulties associated with the learning experience continues to complicate the use of AI tools in these environments.

The ensuing question revolves around the potential of AI, coupled with learning analytics, to tailor guidance based on user knowledge and learning. Can AI leverage its touted abilities in machine and deep learning to detect errors or recurring difficulties, subsequently adapting its feedback to that of a human teacher? We perceive significant risks in relying solely on AI for this purpose. The self-learning capacity of artificial neural networks is constrained by their indexing to an incomplete digital image rather than reality. Additionally, AI's pursuit of concordances between datasets to extract regularities diverges from the maieutics of discrepancies inherent in tutoring practices. As suggested by Savoyant (2006), AI could be useful for a first step: to work out a task (what I have to do), but not to assimilate it (how I have to do it).

While it is conceivable that AI may achieve a level of finesse in
detecting pedagogically relevant discrepancies within texts or simu-
lated activities, challenges persist in indexing AI to remote contextual
elements. In vocational training, the indexing process involves tacit
professional knowledge, which may be distant from the immediate
conditions of action, posing challenges in acquisition, selection, and
relevance that current digitisation capabilities cannot fully automate.

The example of the Silva Numerica project underscores these chal-
lenges. Despite efforts to record learner actions and responses, the
complexity of the resulting data made it challenging for trainers to
conduct effective debriefings. Analytics tied to individual actions proved
difficult to interpret and utilise during training, prompting a shift to
follow learner activity in the virtual environment and incorporate activity
diverse and context-dependent outcomes may be valid, the interpretative
challenge for AI intensifies.

AI's interpretation must acknowledge that conformity with a result doesn't necessarily imply the learner's comprehension of underlying strategies, procedures, or professional knowledge. Moreover, AI's feedback cannot be solely aligned with good practice or standard reasoning, as various other legitimate reasonings may lead to comparable outcomes. Hence, establishing indicators that can evaluate a learner's acquired knowledge or identify difficulties associated with the learning experience continues to complicate the use of AI tools in these environments.

The ensuing question revolves around the potential of AI, coupled with learning analytics, to tailor guidance based on user knowledge and learning. Can AI leverage its touted abilities in machine and deep learning to detect errors or recurring difficulties, subsequently adapting its feedback to that of a human teacher? We perceive significant risks in relying solely on AI for this purpose. The self-learning capacity of artificial neural networks is constrained by their indexing to an incomplete digital image rather than reality. Additionally, AI's pursuit of concordances between datasets to extract regularities diverges from the maieutics of discrepancies inherent in tutoring practices. As suggested by Savoyant (2006), AI could be useful for a first step: to work out a task (what I have to do), but not to assimilate it (how I have to do it).

While it is conceivable that AI may achieve a level of finesse in
detecting pedagogically relevant discrepancies within texts or simu-
lated activities, challenges persist in indexing AI to remote contextual
elements. In vocational training, the indexing process involves tacit
professional knowledge, which may be distant from the immediate
conditions of action, posing challenges in acquisition, selection, and
relevance that current digitisation capabilities cannot fully automate.

The example of the Silva Numerica project underscores these chal-
lenges. Despite efforts to record learner actions and responses, the
complexity of the resulting data made it challenging for trainers to
conduct effective debriefings. Analytics tied to individual actions proved
difficult to interpret and utilise during training, prompting a shift to
follow learner activity in the virtual environment and incorporate activity
data in the post-training evaluation. This pragmatic adjustment highlights the limitations of realising the idealised concept of an intelligent tutor. The integration of AI in professional learning, while holding promise, necessitates cautious consideration of the complex, nuanced nature of real-world professional situations (Casilli, 2019). Sole reliance on AI, devoid of teacher supervision, for learning maieutics and guidance based on discrepancies, remains a formidable challenge. The examples presented underscore the intricate interplay between AI and the multifaceted realities of professional training contexts.

**Conclusion: Navigating the Terrain of AI
in the Field of Professional Learning**

In this chapter, we began our exploration by examining the potential benefits and existing constraints surrounding the integration of AI in the instruction and acquisition of vocational skills within training programmes and workplace contexts (Ciavaldini-Cartaut et al., 2022). We elucidated a pivotal element crucial to unlocking the value of AI in these applications: the imperative of rendering work data, encompassing gestures, situations, circumstances, reasoning, and the learning data itself, both reliable and tangible.

While the trajectory of AI's evolution remains challenging to forecast due to the rapid advancements in machine learning techniques, we contend that a persistent challenge lies in tethering these processes to reality. This challenges the very essence of AI integration, extending beyond a mere technical hurdle to a fundamental, enduring constraint. Consequently, the quest for tangibility and usability of work data mandates a collaborative approach with professional organisations. This involves designing learning support tools that leverage access to such data through collaborative research initiatives, surmounting the socio-technical barrier of professional scepticism towards work data, and facilitating access to AI-embedded work reasoning and knowledge for educators, trainers, and learners.

The realisation of tangible work data necessitates a multifaceted strategy, including the unveiling of real work nuances, the discernment
data in the post-training evaluation. This pragmatic adjustment highlights the limitations of realising the idealised concept of an intelligent tutor. The integration of AI in professional learning, while holding promise, necessitates cautious consideration of the complex, nuanced nature of real-world professional situations (Casilli, 2019). Sole reliance on AI, devoid of teacher supervision, for learning maieutics and guidance based on discrepancies, remains a formidable challenge. The examples presented underscore the intricate interplay between AI and the multifaceted realities of professional training contexts.

**Conclusion: Navigating the Terrain of AI
in the Field of Professional Learning**

In this chapter, we began our exploration by examining the potential benefits and existing constraints surrounding the integration of AI in the instruction and acquisition of vocational skills within training programmes and workplace contexts (Ciavaldini-Cartaut et al., 2022). We elucidated a pivotal element crucial to unlocking the value of AI in these applications: the imperative of rendering work data, encompassing gestures, situations, circumstances, reasoning, and the learning data itself, both reliable and tangible.

While the trajectory of AI's evolution remains challenging to forecast due to the rapid advancements in machine learning techniques, we contend that a persistent challenge lies in tethering these processes to reality. This challenges the very essence of AI integration, extending beyond a mere technical hurdle to a fundamental, enduring constraint. Consequently, the quest for tangibility and usability of work data mandates a collaborative approach with professional organisations. This involves designing learning support tools that leverage access to such data through collaborative research initiatives, surmounting the socio-technical barrier of professional scepticism towards work data, and facilitating access to AI-embedded work reasoning and knowledge for educators, trainers, and learners.

The realisation of tangible work data necessitates a multifaceted strategy, including the unveiling of real work nuances, the discernment
of reasoning processes, and the empowerment of individuals to master
AI as a tool, as articulated by Folcher and Rabardel (2004). We have
underscored the need to collaborate with educators and trainers when
crafting teaching assistance tools as a prerequisite for both seamless
AI integration into pedagogical practices and its general wider accep-
tance. Beyond individual proficiency, the effective utilisation of AI in
learning support requires a broader reconfiguration of tools, fostering an
ergonomic evolution that aligns with the dynamic needs of stakeholders.

In conclusion, through the studies presented, this chapter highlights
the challenges of integrating AI into vocational training: the difficulties
of quality data collection and dealing with the complex interplay between
technological developments, collaborative efforts, and socio-technical
dynamics. The journey forward demands a thoughtful and collabora-
tive approach, navigating the evolving landscape of AI in education and
professional learning.

References

Albero, B. (2019). La théorie de l'enquête: relier les pôles épistémè et praxis de l'activité. *Recherche et formation, 92*. https://doi.org/10.4000/recherche.5651

Casilli, A.-A. (2019). *En attendant les robots. Enquête sur le travail du clic*. Seuil.

Chiron, T. (2018). *Explorer les potentialités d'un Environnement Virtuel Educatif (Silva Numerica) pour favoriser l'apprentissage de situations complexes et dynamiques en lien avec le vivant: le cas d'apprenants forestiers*. Colloque doctoral international de l'éducation et de la formation. Rennes, France. https://hal.science/hal-01767554

Chrétien, F., Métral, J.-F. & Olry, P. (2020). Voir ce qui ne se voit pas. Regarder, voir, savoir en fromagerie. *Revue d'Anthropologie des connaissances*. 14(3). http://journals.openedition.org/rac/10523, https://doi.org/10.4000/rac.10523

Ciavaldini-Cartaut, S., Métral J.-F., Olry, P., Guidoni-Stoltz, D. et Gagneur, C.-A. (2022). L'IA en formation professionnelle : usages, fiabilité des traces d'apprentissage et problèmes posés aux concepteurs et aux enseignants-formateurs. In Romero, M., Heiser, L. et Lepage, A. (Eds). Livre blanc
of reasoning processes, and the empowerment of individuals to master
AI as a tool, as articulated by Folcher and Rabardel (2004). We have
underscored the need to collaborate with educators and trainers when
crafting teaching assistance tools as a prerequisite for both seamless
AI integration into pedagogical practices and its general wider accep-
tance. Beyond individual proficiency, the effective utilisation of AI in
learning support requires a broader reconfiguration of tools, fostering an
ergonomic evolution that aligns with the dynamic needs of stakeholders.

In conclusion, through the studies presented, this chapter highlights
the challenges of integrating AI into vocational training: the difficulties
of quality data collection and dealing with the complex interplay between
technological developments, collaborative efforts, and socio-technical
dynamics. The journey forward demands a thoughtful and collabora-
tive approach, navigating the evolving landscape of AI in education and
professional learning.

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**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Mayen, P., & Lainé, A. (Eds.). (2014). *Apprendre à travailler avec le vivant ? Développement durable et didactique professionnelle*. Éditions Raison et Passions.

Savoyant, A. (2006). Tâche, activité et formation des actions de travail. *Éducation permanente*, **166**, 127-136.

Vadcard, L. (2013). Étude didactique de la dialectique du travail et de la formation au bloc opératoire. *Éducation et didactique*, **7**(1). https://doi.org/10.4000/educationdidactique.1598

Vadcard, L. (2019). *Vers une didactique des gestes techniques. Enjeux pour la formation professionnelle en santé*. Université de Bourgogne Franche-Comté.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.