What we are Working on ...

The purpose of our research is to present how, with the “employment” of digital technology and the contribution of Artificial Intelligence (AI), we can activate leadership and followership schemas and promote awareness regarding implicit theories. We have created a digital tool that can (a) capture students’ leadership and followership implicit theories via the creations of two avatars, (b) help students reflect on their implicit theories via provision of tailored feedback on the created avatars and (c) elicit interesting discussions and foster social awareness within the classroom.

Research has provided empirical evidence that individuals hold internal representations to describe and label others as leaders (Eden & Leviatan, 2005) or as followers and those representations will influence their interpersonal relations (Coyle & Foti, 2015; Dienesch & Liden, 1986; Engle & Lord, 1997; Lord et al., 1984; Lord & Maher, 1993). Individuals are also interpreting social processes such as leadership or followership based on implicit representations or schemas (Alabdulhadi et al., 2017). Implicit theories are the framework based on which individuals perceive, internalize and socially determine leadership and followership. Implicit leadership and followership theories are defined as the cognitive structures that determine the traits and characteristics of leaders (Lord et al., 1984; Lord & Maher, 1993) or followers (Sy, 2010) respectively. These cognitive knowledge structures or schemas are developed through the socialization process and cumulative experiences, stored in the memory and are re-activated when an individual interacts with an actual leader (Epitropaki & Martin, 2004) or a follower (Sy, 2010). In other words, those schemas participate in a fast and efficient manner of assessing, describing and labeling others as leaders (Eden & Leviatan, 2005) or followers by their holders that are not always aware of them (Schyns et al., 2011).

To uncover their implicit schemas, we will ask students to create their own leader and follower avatars. They could choose several features that are either inherent like skin color and facial expressions, or more external like clothes and glasses (Ducheneaut et al., 2009; Hooi & Cho, 2017). By offering Avatar’s Item Customizability (AIC, Teng, 2021) we provide participants with autonomy and freedom to disclose their preferences (Valenzuela et al., 2009), engage with the process (Rubin, 1993) and “voice” their personal style or artistic point of view (Teng, 2021). According to the agency model of customization, participants are the source of information required to proceed with customization (Sundar, 2008). By providing the freedom of creation, students will most likely use their implicit schemas as their source of inspiration to customize their avatars (Hooi & Cho, 2017; Teng, 2021).

Generative Artificial Intelligence (Gen AI) with the use of Large Language Models (LLM), will automatically provide to students a tailored and real-time feedback regarding their avatars as explicit representations of a leader and a follower. The Image to Text AI (I2T AI), by analyzing images along with the evaluation of data patterns from Gen AI, can offer insights to students about their choices that might not have been obvious to them, thus raising awareness on their own implicit leadership and followership theories.

In addition, students can share with others, in small group settings, their avatars as well as the feedback they have received from AI. We advocate that this cooperative interaction among students promotes a retroactive exchange of beliefs and ideas enabling social awareness that advances learning (Askew, 2000; Cohen, 1994; Kolb & Kolb, 2005, 2009). AI feedback is powerful, objective and data driven but if we combine it with human intuition, we can deliver a more broad and valuable feedback and ensure empathy and better understanding.

According to Burgoyne and colleagues (2004), feedback is essential for learning and development. Through the use of digital technology and with the support of Artificial Intelligence we can address several theoretical issues such as participants’ self-awareness regarding their implicit leadership and followership theories, social awareness of others’ implicit theories and knowledge about how their salient beliefs about leaders and followers are perceived by others. Be aware of the different implicit leadership and followership theories is an essential step towards personal change and leader identity development (DeRue & Ashford, 2010; Schyns et al., 2011).

Investigating the possibility of constructing an approach for business to "on-board" themselves in Generative Artificial Intelligence (Gen-AI) and an evaluation framework on selecting a Large Language Model (LLM) depending on the use case.

The approach is focusing on:

• a short-term "quick win", focusing on effort, CapEx and Time-to-Market, adoption that will allow for a "Test & Learn" capability to familiarize all stakeholders with the concepts, potential and associated Risks.
• a strategic approach of adoption in addressing data confidentiality and privacy along with other Risks eg OpEx.
• an evaluation framework along with a balanced scorecard that will enable discission-makers to identify, evaluate and ultimately select the most appropriate model for each use case, since the "latest and finest" might not be the proper approach.

An investigation into the effects of integrating basic generative AI prompts into a flipped classroom methodology for the students point of view. The flipped classroom approach, rooted in constructivist learning theories, involves students engaging with digital resources before class to learn foundational concepts. Generative AI tools like ChatGPT offer new possibilities for personalizing and enriching these pre-class and in-class components through customized content generation and interactive simulations. An experimental study is being conducted with undergraduate students to examine whether implementing generative AI prompts as supplementary resources in a flipped classroom environment could enhance student engagement, learning outcomes, and overall educational experience compared to a traditional flipped classroom approach without AI integration. Both quantitative and qualitative data will be collected and analyzed.