Conference Agenda

Introduction

In today’s quickly evolving technological landscape, engineering graduates must be able to understand the fundamentals of cutting-edge techniques. Under this perspective both students and faculty must explore the digital world and artificial intelligence as allies in technological innovation. As the demand for Artificial Intelligence (AI) expertise continues to rise, equipping students with essential AI competencies has become imperative. For their final capstone projects, engineering students were asked to come up with an innovative system, under the supervision of four faculty members. This paper presents the student’s learning experience while designing AI based systems in real-world scenarios and resulted in fostering hands-on experience in novel technologies.

Methods

Currently, students have free access to tools for the development and innovation of technology using artificial intelligence such as neural networks. Final year engineering students were tasked with developing a capstone project to showcase their skills and were given ten weeks for ideation, implementation, and testing. Some of these teams decided on the integration of AI into their design. This required students to conceptualize, develop, and execute projects integrating AI solutions. For the product design using neural networks, there are many free access tools and algorithms, making the technology collaborative with teaching techniques.

First, students followed an introductory course on the use of AI and AIoT systems. This first interaction guided them through the use of accessible tools such as Google’s Teachable Machine, and Edge Impulse. While the students recognized the ease of use of these tools, they quickly outgrew them and had to find other alternatives to suit their design requirements.

Students had to evaluate the effectiveness of their respective systems. This opened the door to reinforce important concepts on AI such as ROC curves and confusion matrices. In this way, an open-ended self-assigned project may be guided towards the completion of learning objectives.

Results

Students presented three projects which depended on the use of AI. (i) An automated inventory system with object and speech recognition, (ii) a Human-Robot interactive tool capable of differentiating hand gestures, (iii) Vehicle and pedestrian detection system. The students’ exploration was mostly self-guided, allowing them to increase their confidence and be responsible for the learning process. The implementation of AI techniques felt novel and was able to keep them engaged on the task.

The projects not only demonstrated technical proficiency but also underlined the students' ability to think critically, and to apply AI methodologies to solve real-world challenges through collaborative efforts. In this way, students gained practical insights into the challenges of implementing AI technologies.

In conclusion, the outcomes of this capstone project underscore the necessity for integrating AI courses into the academic curriculum. The experiences gained through project-based learning offer a valuable foundation for students to grasp the complexities of AI technologies and their practical applications which are essential for preparing students for joining the workforce. This paper advocates for the integration of AI in higher education to empower students with the skills needed to navigate the dynamic landscape of emerging technologies regardless of their disciplines.

Engineering students frequently encounter difficulties with numerical and analytical techniques, perceiving them as unengaging or overly theoretical and failing to see the relevance to engineering contexts or their future careers. The issue may be exacerbated by the use of traditional didactic teaching methods that often emphasize recall and computational rigor over comprehension and the capacity to adapt and apply the methods learnt to new contexts.

This paper details the approach taken to teaching a third-year module ‘Performance Engineering’ at Nottingham Trent University. By structuring learning around practical scenarios and introducing technical methods in the context of solving applied problems, students are better able to contextualize their knowledge. Flipped and active learning techniques are used to enhance conceptual comprehension and ensure students are able to apply the methods learnt beyond the contexts presented.

In recent years, the demands placed on engineers and designers have undergone a significant transformation, largely due to the increased reliance on computer-aided engineering (CAE) systems, particularly in the fields of mechanical engineering and design. The fusion of foundational knowledge with the operational methods of CAE systems has become intrinsic to the modern work environment, making CAE education an indispensable component of engineering and design curricula. However, devising and implementing educational courses in this domain has proven to be a formidable challenge, as many core principles are closely intertwined with software applications, necessitating a seamless integration of theory and practical application.

The primary objective of this research was to develop an educational concept that establishes a robust connection between theoretical fundamentals and the hands-on utilization of CAE programs. This entailed not only the dissemination of theoretical knowledge but also the practical application of acquired skills in areas such as advanced computer-aided design (CAD) methods, finite element analysis (FEA), and dynamic system simulation using selected high-end simulation software.

The research centralizes questions related to the development of an educational framework that empowers students to master various CAE programs, bridges the gap between theory and practice, and encourages an environment that fosters experimentation. This approach is rooted in the belief that students benefit most from hands-on exploration and testing. Moreover, the importance of creating a conducive learning environment and the implementation of a robust system for performance assessment was recognised.

To realise these educational goals, a multifaceted teaching concept was crafted. The approach encompasses traditional lectures to establish a solid theoretical foundation, a flipped classroom methodology that acquaints students with simulation environments, and project-based learning to apply acquired knowledge through real-world examples. The e-learning component allows students to tailor their learning environment and access various learning materials, promoting interaction among peers. Furthermore, a mentoring program, "Meet the Experts," was introduced, which serves students with a heightened interest in the subject matter.

The evaluation of this educational concept relied on data obtained from student surveys and examinations conducted over the past several years. The results affirm that the developed teaching approach successfully bridges the gap between theory and practice in engineering education. Beyond its immediate applicability to engineering, this approach can be adapted to other disciplines, extending even to fields that involve the amalgamation of theoretical and practical software applications, such as artificial intelligence. CAE education remains a fundamental element in engineering instruction and can serve as a model for other technical disciplines grappling with analogous challenges.

Future research endeavors include an ongoing monitoring of student progress and an exploration of the optimal balance between practical and theoretical content to refine our teaching concept.

Artificial intelligence (AI) is increasingly expanding its capabilities and presence in fields because today's industry demands working with big data to extract meaningful user insights to align its goal with success. The government sector, specifically the intelligence community (IC), is not an exception to this need. The challenge for data analysts in the sector is finding relevancy within such a large dataset through searching, sorting, and contextualizing, which requires categorizing and summarizing results at the end. The efficiency of built-in AI to organize and generate a natural language for a human user became an essential topic for investigating a learning process for User Experience (UX) design students in college when integrating AI models within interface designs. The study partnered with the IC partners and set up a conceptual enterprise dashboard project to answer the following research question: How might UX design students improve their learning experience when speculating an integration of the AI model within an application to search, triage, and contextualize data for the IC analysts with a lack of user data? Instead of emphasizing the conceptual design solution, the study focused on improving the student's educational experience of navigating ambiguity built into the AI project to enhance the human experience of interacting with the system.

Eleven students were assigned into three groups of three to four working on different personas and had access to the same proxy datasets from the sponsors. The students delivered the nine-week project with design artifacts like value propositions, market research, questionnaires, personas, scenarios, mappings, flow charts, wireframes, UI components, prototypes, user testing, and UX documentation with guidance from a graduate assistant and a principal investigator. During each phase of the Design Thinking (DT) process, the students discussed the difficulties of navigating through AI conceptual solutions because of the user data gaps in the brief due to the confidentiality required in the Intelligence Community and the nature of the innovation. The study utilized the DT and Design Inquiry of Learning (DIL) framework to identify role-playing and storytelling activities to enhance student's learning experience by mitigating frustrations exhibited in speculating AI dashboard interface design.