Three forms of contemporary influence

"The past tends to regain its lost influence by actualizing itself."

In our modern, interconnected societies, the dynamics of influence have evolved to occupy a central place in human interaction. This process of influence manifests itself in a variety of forms, affecting both individual behavior and collective decisions.

Influence can be expressed through three emblematic figures that characterize this era: the collective intelligence facilitator, the generative AI and the social influencer. These three bodies represent distinct but interconnected approaches, each with their own particular effects on the processes of learning, motivation and knowledge co-construction.

1. The collective intelligence facilitator - Influence through co-construction and participation

The collective intelligence facilitator embodies a subtle, diffusive form of influence, focused on mediation and the co-construction of knowledge within groups. Unlike vertical power figures, the facilitator adopts a coaching posture, creating an environment conducive to active, egalitarian participation.

The influence exerted by the facilitator relies on relational skills such as listening, empathy and the ability to reformulate exchanges to encourage the emergence of ideas. This role is essential in educational and training contexts, where the aim is to enhance distributed intelligence within a group. As Pierre Lévy (1997) has shown, collective intelligence resides not only in individuals, but also in the interactions that emerge from them. In this sense, the facilitator does not impose his or her knowledge, but guides the group towards the construction of collective solutions. This approach produces a learning dynamic in which each individual becomes aware of his or her own skills and limits, thus reinforcing involvement and motivation to learn.

The facilitator's influence manifests itself mainly through the establishment of a secure, respectful framework, where exchanges can take place freely and where each member is encouraged to contribute actively. Learning thus becomes a dynamic, collaborative process, where collective reflection feeds individual development.

The effect on motivation is directly linked to the recognition of each individual's skills, and to the feeling of belonging to a group that is creating common knowledge together. It is in this interconnection that the true power of the facilitator's influence lies: an influence that stimulates critical thinking and collaboration, and fosters sustainable learning based on mutual respect and co-creation.

2. Generative AI - influence through abundance of content and speed of execution

Generative AI, on the other hand, embodies a different kind of influence. It relies on the use of algorithms and statistical models fed by massive volumes of data. AI generates responses, suggestions or content almost instantaneously, creating a source of influence through the quantity and diversity of information offered.

This form of influence is particularly effective in stimulating curiosity and exploration. In a matter of seconds, AI can provide new ideas, summaries of articles, or even examples to further explore a topic. This ability to provide vast and varied content fuels interest and motivation to learn. However, this form of influence also presents challenges. Generative AI, while producing impressive responses in terms of speed and breadth, offers no guarantees as to the veracity and relevance of its answers. It is essential that the user maintains a critical stance towards what is proposed.

Generative AI encourages rapid exploration, but it also imposes heightened vigilance, prompting critical reflection and verification of sources. Users may be tempted to accept without examination what is given to them, but true learning only takes place when these suggestions are confronted with personal reflection and prior knowledge. Paradoxically, while AI facilitates access to a wide range of content, it also demands greater intellectual responsibility on the part of those who use it, in order to avoid superficial learning.

3. The social influencer - an affective and identity-based influence

Finally, the social influencer is another powerful but distinct form of influence. Unlike the previous forms, the social influencer relies on affective and identity-based mechanisms to unite a large audience around his or her person and ideas.

Influencers build an intimate bond with their followers, not only through the content they share, but also through the projection of a personal, empowering image. This relationship is often nurtured by a strong emotional dimension, where adherence is based more on identification and admiration than on analytical or critical reflection.

The social influencer generates a form of learning that could be described as "emulated". His or her subscribers seek to reproduce his or her behaviors, consumption habits or values, thus creating a phenomenon of social mimicry. This form of influence can trigger an openness to new practices or ideas, particularly in areas such as sport, nutrition or culture. However, it also entails risks, notably that of blind imitation and dependence on external models. Learning in this context can be limited to passive reproduction of the model's ideas or behaviors, without any real personal construction of thought.

The role of the influencer in learning motivation therefore lies in the ability to arouse a desire to imitate and identify with a charismatic figure. However, one of the major challenges is to maintain a certain intellectual autonomy and not allow oneself to be locked into a relationship of excessive dependence on this figure. This form of influence, although powerful on an emotional level, must be counterbalanced by a return to personal reflection and critical distancing from the models proposed.

A collective dynamic

By promoting egalitarian participation and the co-construction of knowledge, the collective intelligence facilitator invites us to rethink learner-centered teaching practices. This model highlights the need to develop spaces for collaborative exchange and shared reflection. In this dynamic, education is no longer limited to the vertical transmission of knowledge, but becomes an interactive process in which the learner is at the heart of knowledge creation.

The use of methods such as design thinking or brainstorming, which encourage collective creativity, becomes essential in a pedagogical framework. This type of approach contributes to the development of cross-disciplinary skills such as critical thinking, collaboration and autonomy, which are crucial in today's professional and social world.

Generative AI, while raising ethical and critical concerns, can also play a significant role in education. Its ability to rapidly provide varied resources, generate ideas and support autonomous exploration can enrich learning pathways. However, for its use to be beneficial, it needs to be framed by pedagogical practices that encourage critical analysis of the information produced. Trainers need to integrate reflective exercises that enable learners to confront AI-generated data with their prior knowledge, in order to prevent a passive, fragmented approach to learning.

As for the social influencer, although he or she is often perceived as a figure of consumption and spectacle, his or her effect on learning dynamics should not be underestimated. Through their ability to unite a public around values and concrete examples, influencers can be a source of inspiration.

In the educational context, they can offer motivating resources, innovative practices or role models that arouse learners' curiosity. However, there is a danger in over-identifying or failing to think independently. It's up to educators to integrate this affective dimension of learning, while encouraging critical thinking and the personal construction of knowledge.

Illustration: Stefan Schweihofer - Pixabay

Sources

Lévy, P. (1997). Collective intelligence: Mankind's emerging world in cyberspace. Perseus Books.
https://amzn.to/3D6MmI0

Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge University Press.
https://amzn.to/4kb40Lr

Winograd, T., & Flores, F. (1986). Understanding computers and cognition: A new foundation for design. Ablex.
https://amzn.to/4hM8OoS