Implementing technologies in business schools can bring significant benefits to students, faculty, and other staff. However, common mistakes can hinder the effectiveness of new technology. These mistakes include underestimating the importance of user training, neglecting students’ needs, ignoring ethical concerns, failing to monitor and evaluate technology’s impact, and inadvertently widening resource access inequalities. Successful implementation of technology needs a clear plan, personalised training, ethical reflection, continuous assessment, and equitable access strategies. While new technology such as AI can not yet replace humans, it can significantly enhance the learning experience when implemented thoughtfully and inclusively.
Higher education institutions, and in particular business schools, face the crucial task of adopting and implementing new technologies to help students acquire a wide range of technical, social, and emotional skills.
This transition is not without its challenges. It is common to see institutions tempted by tech fads and ‘communication stunts’ to create a buzz but then fail to deliver benefits.
Here are five mistakes commonly made when introducing new technologies for teaching and learning. In this piece, I draw upon our recent experiences experimenting with Generative AI (GAI)- powered teaching assistants at the ESSCA School of Management.
The five classic mistakes:
1. Underestimating user training and digital culture
One of the most common mistakes is to underestimate the importance of teacher and student training. Integrating new technologies requires a thorough understanding of why they are being chosen, how they work, and how they can be used. Too often, institutions fail to provide adequate training.
Training for students is often neglected on the pretext that they are ‘born’ with these technologies. This may be true, but it does not make them seasoned users – far from it. Personalised training that considers the needs and sensitivities of each individual leads to greater commitment to any project and avoids resistance to change. Following Karsenti’s model1, this avoids a phase of deteriorating performance and experience (for teachers and students). Furthermore, in education, technology is everybody’s business, including administrative staff, who are an important part of the value chain.
Faculty members unfamiliar with the advanced features are likely to use only the most basic functions of the tools or not use them at all. The same applies to students. A more cautious approach involves appropriate training or awareness-raising programmes, enabling optimal use of technology and improving the learning experience.
2. Not taking sufficient account of students’ needs
Another common mistake is to neglect the learning needs of students in the process of integrating technologies. Technologies can positively transform the learning experience, but only if they meet the needs and preferences of learners.
- Do they work better?
- Do they save time?
- Do they enable deeper learning?
- Are they more inclusive?
- Are they more flexible?
To ensure successful adoption, institutions need to actively solicit students’ feedback and involve them in the tool selection process.
Business school administrators may be convinced in good faith that these tools will improve the student learning experience by offering, for example, easy access to resources, online collaboration tools and opportunities for interactivity.
However, during the deployment phase, it is not uncommon to find that the tools do not fully meet students’ needs. Some students may complain about an unintuitive user interface, which makes it difficult to navigate and find relevant content. Others may report compatibility problems with their devices, which limits their access to the platform. Still, others may feel that the tool lacks interactivity and does not allow for effective engagement on their part, undermining their motivation and commitment.
Without adequate consideration of students’ needs, the tools’ functionalities may not fully correspond to their preferences, leading to limited use and reduced adoption.
3. Neglecting ethical concerns
Recent technologies, and in particular generative AI, raise important ethical issues, particularly regarding data confidentiality, the protection of privacy, users’ intentions and the consequences of their actions.
Everyone, particularly teachers and students, needs to be trained and made aware of these aspects. What am I trying to do by using ChatGPT? Work less? Work faster? Make people think I am capable when I am not? Equally, on the ethics of consequences, what are the consequences of my actions if I use ChatGPT without telling the teacher? It is essential to include ethical reflection right from the initial planning and development stage of any integration.
If these tools incorporate artificial intelligence, concerns arise about academic integrity and data confidentiality. Students are likely to use these tools to produce their work without being aware of these issues since the text produced by AI is not the result of plagiarism, even if they are not the authors. The same applies to teachers. Ethical reflection must be included right from the initial planning and development stage. This reflection, which validates the initial stages, must be accompanied by training in the ethical use of AI for students and teachers.
4. Lack of monitoring and evaluation
A common mistake is a lack of monitoring and evaluation of integrated technologies’ effectiveness. To ensure long-term adoption, institutions need to put in place robust monitoring and evaluation mechanisms to measure their impact on teaching and learning. This involves collecting relevant data, analysing students for aspects like learning assessments, and continually adjusting technology strategies based on the results.
The aim of introducing technological tools into higher education institutions is to improve student learning (classroom interaction tools, LMS, video platforms, etc.) or to facilitate the work of lecturers (online grading grids, discussion forums with students, etc.). But are the objectives being achieved? An online grading system can be tedious for teachers if it is badly designed or if it does not correspond to the way they grade. Even if the needs are correctly identified at the initial stage of a project, several factors may make the system complicated to use. It is therefore necessary to consult users quickly to obtain tangible data on the usefulness (is it better with than without) and effectiveness of the tool (speed, readability). Feedback from students on the tools designed for them is just as important, but measuring effectiveness is more complex. An interactive tool may be fun, easy to use and accessible, but does it improve learning? This is what needs to be measured.
5. Widening inequalities in access to resources
Finally, the introduction of technologies, and in particular GAI, can inadvertently widen the gaps in access to resources. The costs associated with the purchase of subscriptions, where applicable, and knowledge of the tools may exclude certain students, particularly those from disadvantaged socio-economic backgrounds. Without an equitable strategy for technological integration, higher education institutions risk reinforcing already existing disparities in access to education, thereby compromising their mission of inclusion and equal opportunities. It is, therefore, crucial that institutions take steps to ensure that all student populations can benefit from the advantages of recent technologies.
Access to digital resources, whatever they may be, is, of course, a question of income (owning a personal computer to access the learning management system from home, owning a telephone to access classroom interaction tools or to scan QRs to prove attendance at class), but it is also a question of digital culture and socio-professional background. Recent studies2 have also shown that many female students feel less comfortable with technology than their male counterparts. This is attributed to a self-selection bias in which women tend to underestimate their own technological skills. Consequently, without an equitable technology integration strategy covering all these aspects, the institution runs the risk of further marginalising students and compromising their chances of academic success.
Conclusion
Introducing technology into an educational environment is complex, especially when the issues at stake concern the learning of students from diverse social and geographical backgrounds.
Adoption depends on being truly clear on how the technology will help intended users and the learning process. The worst thing you can do is to be swept along simply because technology is the latest thing. As AI develops and evolves, ethical considerations must be central to your decision-making at every stage. Finally, monitoring and adaptation must become a regular habit.
Our experience at ESSCA is that a chatbot (or indeed any existing technology) cannot get anywhere close (yet) to replacing a teacher. It cannot respond without the qualitative input or innate understanding of a human, but it has the potential to serve as a valuable assistant to students and teachers alike if the five mistakes above are avoided.
Footnotes
1 Karsenti, T. and J.Bugmann (2018). ASPID: unmodèle systémique des usages du numérique en éducation. Dans S. Lacroix et Y. Tomaszower (Eds.), Le numérique (pp. 47-61). Paris, France: Editions EPS.
2 Sobieraj, S. and N. Krämer (2020). Similarities and differences between genders in the usage of computers with different levels of technological complexity, Computers in Human Behavior, Volume 104.
Stéphane Justeau is Head of the Institute of Advanced Pedagogy, ESSCA School of Management, France.
