AI-Powered Tutoring in Engineering: Personalized Guidance for Student Success


The world of education and career-building has undergone a seismic shift ever since AI has re-entered the technological revolution. A key enhancement has been the use of AI tutoring in education. STEM subjects have also seen a significant boost from it, with AI tutoring in engineering. This has opened endless possibilities for engineering students, who can use AI in theoretical learning and practical application of their skills in their education.  

For students, it has created a plethora of opportunities to enhance their knowledge and skills. AI has also piqued students’ interest and new ways to leverage AI for career development have also emerged. AI tutoring has also become relevant in the many different aspects of individual learning and organizational development. With the advent of bootcamps that can help increase one’s scope of career building, specialized programs like software engineering bootcamps have also garnered attention. 

Since AI can provide personalized and extremely tailored learning solutions, it has become imperative to understand how one can venture into understanding AI tutoring as a student. 

Diversity Challenge: Not only do students come from varied backgrounds, the scope of experiential learning is also a challenge since there are different expectations from different students when it comes to education. An article in the Harvard Business Review pointed out that data gathered from chatbots could be used to create “new innovative services and programs to further improve students’ educational experiences.” 

Online Resources: AI tutoring tools can equip learners with new-age skills and cover a variety of resources with a personalized approach to learning. For example, TensorFlow is a “Python-friendly open-source library”. It lets developers employ Tensorboard to create graphic visualization and construct neural networks.  

For those interested in Machine Learning Engineering, another such tool is AutoML that helps to resolve real-world problems using the techniques of machine learning. 

Practical Solutions: Engineers are taught to provide lasting real-world solutions, and hence need a lot of hands-on practice to enhance their skill set. This can be done by using AI to simulate situations based on problem-based learning, wherein students can team up with their peers to analyze, understand, and solve problems based on practical scenarios. 

This can help incorporate their learning with inter-personal skills and develop solutions in a time-limited high-intensity setting to prepare them better for their career. One such example is found in Universidad Polite´cnica de Madrid wherein students visualize and create the process of designing, manufacturing, and validating unique medical devices and test their economic viability. 

Unlike institutional learning, experiential learning focuses on the cross-vertical collaboration of various stakeholders like financial investors, technical ideators, creators, governmental agencies, etc., so it becomes imperative that students have a holistic understanding of each of these stakeholders to provide holistic solutions. 

Educator Support: Educators are not only tasked with teaching, but also with assessing students, ensuring optimum learning and troubleshooting, and creating an environment of learning that is conducive to a diverse pool of students. 

AI models can be used to create mock question papers with a steady randomization of questions from a big pool, they can be used to track and identify patterns in student learning based on their assessment results and help bridge these gaps with learning facilitated by educators. Thus, AI can help in the effective management of administrative tasks which can in turn enable educators to spend more time and effort into creating learner solutions more efficiently. 

 The scope of using generative AI in engineering is endless. It can range from writing code to analyze data, to designing a product and multiple iterations to include various concerns. Generative AI can effectively utilize the power of LLMs and generate outputs that can meet their individual needs. 

Engineering needs form the very foundation of our society. Software engineering is the backbone of our technological needs, civil and mechanical engineering is the foundation of our society, biochemical engineering has enabled us to live long, healthy lives, and so on. Thus, it has not only become imperative, but also birthed the need for engineering students to employ AI into helping find innovative and sensitive solutions that have room for iterations and adjustments. 

A student’s motivation is multiplied when they succeed at practice problems and can be adversely impacted if a student does not succeed. 

Learners not only come from different backgrounds, but they can also have different learning curves and hence have different learning needs. However, being put in the same learning environment does not cater to everyone’s needs. A cookie-cutter approach to learning leaves much to be desired. Thus, if a heterogeneous group of students work on the same problem, it can yield various solutions. Unless a problem objectively aims to achieve only one solution, it is not possible to reconcile different solutions under one umbrella. 

Thus, the need for AI grows exponentially to enable learners and educators to deliver individualized learning and teaching programs. The above-mentioned study highlights a specific example of this concern. Creating images and videos from textual inputs is seeing great growth in diffusion models. AI tools can be “used to generate 3D models of different bridge designs for an engineering statics course.” 

Similarly, for software engineering aspirants, picking out bugs from codes can be a time- and labor-intensive process, sometimes spanning weeks of sprints and insufficient results. However, using AI to do a quick sweep to recognize problems and offer solutions can significantly reduce these error margins, and engineers can prospectively choose the best possible solutions based on the context and needs of the program. 

Deep learning solutions can help make sense of abstractions and incorporate natural language processing, with the added benefits of speech and image recognition. 

AI can offer a host of learning solutions, from processing online documents and recordings, ingesting large amounts of content, and assessing a bulk of data to offer an exhaustive range of inferences. However, human intervention is an uncompromisable aspect of employing AI in learning, along with dealing with the probability of ethical dilemma. 

As is with any unmonitored innovative solutions in the new age, the responsibility of ethical utilization of such resources should also be considered. Students and teachers alike need to assume the said responsibility and act ethically when working with AI. In this era of unfettered innovation, the ethical use of AI is paramount for both educators and students as harnessing AI-driven learning and innovation can propel one’s technical prowess.

Therefore, working with AI and leveraging it for learning and innovation can be a strong suit in your technical skills. Being AI-driven can help you be ahead of the industry and deliver bespoke solutions and mitigate crises efficiently and innovatively.