Introductory Artificial Intelligence Research Articles

AI frontiers in oncology: Bridging the gap between humanity and machine.

e13657 Background: Artificial intelligence (AI) is rapidly transforming medical education and how healthcare services are delivered. Oncology healthcare professionals (HCPs) can significantly benefit from the advances of AI including optimized diagnostic capabilities, treatment plans, resource allocation, patient outcomes and much more. The synergy between technology and healthcare is strengthening, heralding opportunities for a revolution in medical education and the broader healthcare industry. We developed an introductory AI workshop and conducted pre- and post-surveys, aiming to inform the design of successive workshops and an AI curriculum, to access knowledge, attitudes and perceived challenges for AI integration into Medical Oncology. Methods: An interactive one-hour workshop was conducted for physicians and faculty of the Hematology and Oncology department. 18 participants, encompassing 6 physicians and 12 other HCPs completed a pre- and post-workshop survey. We evaluated participants' knowledge, opinions, and perceived limitations regarding AI in oncology research, practitioner education, and clinical applications using both open and closed-ended questions, such as multiple-choice and 1-5 Likert scale formats. Results: Survey results included 18 pre-workshop and 17 post-workshop participants, 83% had no formal computer science training: AI knowledge in medicine and awareness of clinical AI applications increased by 100%. Interestingly 24% felt the workshop influenced their view on AI's role in their medical career and better prepared for AI-related challenges. 58% believed AI would assist clinicians within 5 years. However, the belief that AI will play a significant role in their own career did not change after the intervention. Data privacy concerns increased by 25%, while concerns about physician over-reliance on AI decreased by 15%. Notably, 76% were motivated to engage with AI during their future practice after the workshop. Conclusions: The AI initiative significantly enhanced oncology healthcare professionals' understanding and readiness for AI integration, despite most lacking prior formal AI education. A marked increase in AI knowledge and motivation to use AI in practice was noted, although perceptions of AI's impact in their day-to-day work remained unchanged with one workshop, contrasting expert views and predictions. Data privacy concerns rose, but decreased fears of over-reliance on AI indicate a more balanced perspective. These findings stress the need for ongoing AI education in healthcare and underscore the importance of baseline evaluations and feedback for shaping future workshops. Since, AI's integration into society is poised to transform healthcare, an AI curriculum is essential.

Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks.

Artificial intelligence (AI), or deep learning (DL), approaches have already found their way into our everyday lives. Furthermore, these methods are a central part of research in the life and natural sciences and have been applied in the form of machine learning for decades. In pharmaceutical and medicinal chemistry, and in computer-aided drug discovery, current developments are also changing the way drugs are developed. It is essential to familiarize students with AI methods already during their studies and prepare them for future tasks and challenges. We developed a set of interactive learning materials based on cheminformatics examples that can be used to establish such introductory AI courses in the life and natural sciences. These interactive notebooks are easily accessible without the need for installation, and no prior programming knowledge is required. Through these notebooks, students can easily study how AI/DL works and how these methods can be applied. This knowledge will foster a general competence when interacting with and evaluating future DL applications later in their career. The materials are freely available and publicly accessible through a GitHub repository in German and English.

An Artificial Intelligence Training Workshop for Diagnostic Radiology Residents.

To develop, implement, and evaluate feedback for an artificial intelligence (AI) workshop for radiology residents that has been designed as a condensed introduction of AI fundamentals suitable for integration into an existing residency curriculum. A 3-week AI workshop was designed by radiology faculty, residents, and AI engineers. The workshop was integrated into curricular academic half-days of a competency-based medical education radiology training program. The workshop consisted of live didactic lectures, literature case studies, and programming examples for consolidation. Learning objectives and content were developed for foundational literacy rather than technical proficiency. Identical prospective surveys were conducted before and after the workshop to gauge the participants' confidence in understanding AI concepts on a five-point Likert scale. Results were analyzed with descriptive statistics and Wilcoxon rank sum tests to evaluate differences. Twelve residents participated in the workshop, with 11 completing the survey. An average score of 4.0 ± 0.7 (SD), indicating agreement, was observed when asking residents if the workshop improved AI knowledge. Confidence in understanding AI concepts increased following the workshop for 16 of 18 (89%) comprehension questions (P value range: .001 to .04 for questions with increased confidence). An introductory AI workshop was developed and delivered to radiology residents. The workshop provided a condensed introduction to foundational AI concepts, developed positive perception, and improved confidence in AI topics.Keywords: Medical Education, Machine Learning, Postgraduate Training, Competency-based Medical Education, Medical Informatics Supplemental material is available for this article. © RSNA, 2023.

Effect of groups size on students' learning achievement, motivation, cognitive load, collaborative problem‐solving quality, and in‐class interaction in an introductory AI course

AbstractBackgroundGroup size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem‐solving process.ObjectivesThis study investigated the effect of group size in a K‐12 introductory Artificial Intelligence course by comparing the students' cognitive load, learning motivation, collaborative problem‐solving quality, and in‐classroom interaction between two‐ and three‐student groups.MethodsForty‐eight high school students were randomly assigned to two kinds of groups (i.e., the two‐student group, and the three‐student group, each consisting of 24 students). During the experiment, Xiaofei robots were used to teach the theoretical and practical content of five AI topics over 6 weeks for 1.5 h each week.ResultsThe ANOVA results indicated that group size mattered in the AI course, the two‐student group was more effective in terms of enhancing students' learning motivation and collaborative problem‐solving quality, as well as imposing more cognitive load than the three‐student group. The advantage was more obvious in the practical problem context. The Lag Sequential Analysis results indicated that more collaborative learning behavioural sequences existed in the two‐student group than in the three‐student groups.ContributionThis research provides empirical evidence and potential guidance for group design in Artificial Intelligence Education. Although group size did not affect students' learning achievement, it affected learning motivation, cognitive load, and problem‐solving quality and processes. Two‐student groups work better than three‐student groups in the AI course.

Teaching Introductory Artificial Intelligence through Java-Based Games

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

Performance Evaluation of LSA, NMF and ILSA in Electronic Assessment of Free Text Document

Aims: To evaluate the performance of an Improved Latent Semantic Analysis (ILSA), Latent Semantic Analysis (LSA), Non-Negative Matrix Factorization (NMF) algorithms in an Electronic Assessment Application using metrics, Term Similarity, Precision, Recall and F-measure functions, Mean divergence, Assessment Accuracy and Adequacy in Semantic Representation.
 Methodology: The three algorithms were separately applied in developing an Electronic Assessment application. One hundred students’ responses to a test question in an introductory artificial intelligence course were used. Their performance was measured based on the following metrics, Term Similarity, Precision, Recall and F-measure functions, Mean divergence and Assessment Accuracy.
 Results: ILSA outperformed the LSA and NMF with an assessment accuracy of 96.64, mean divergence from manual score of 0.03, and recall, precision and f-measure value of 0.83, 0.85 and 0.87 respectively.
 Conclusion: The research observed the performance of an improved algorithm ILSA for electronic Assessment of free text document using Adequacy in Semantic Representation, Retrieval Quality and Assessment Accuracy as performance metrics. The results obtained from the experimental designs shows the adequacy of the improved algorithm in semantic representation, better retrieval quality and improved assessment accuracy.

Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey.

The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude. A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated. Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased. A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.

Writing and speech instruction in an introductory artificial intelligence course

Providing opportunities in both written and oral communication continues to grow in importance within the undergraduate Computer Science major. An introductory course in Artificial Intelligence tha...

A.I. as an Introduction to Research Methods in Computer Science

While many computer science programs offer courses on research methods, such classes typically tend to be aimed at graduate students. In this paper, we propose a novel means for introducing undergraduate students to research experiences in computer science — via an introductory Artificial Intelligence (A.I.) course. Students explore the content areas typically covered in an upper-level A.I. course (heuristic search, constraint satisfaction, game-playing etc.), while also learning about the mechanics of how empirical research is conducted in this field.

ACTIVE‐ating Artificial Intelligence: Integrating Active Learning in an Introductory Course

This column describes my experience with using a new classroom space (the ACTIVE Center), which was designed to facilitate group‐based active learning and problem solving, to teach an introductory artificial intelligence course. By restructuring the course into a format that was roughly half lecture and half small‐group problem solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class.

Teaching With Watson


 
 
 In this paper, we describe how we integrated the materials from the 2013 IBM The Great Minds Challenge (TGMC) - Watson Technical Edition into our Introductory Artificial Intelligence course. We describe the variety of materials made available by IBM, as well as the nature of the competition and the datasets that are at the heart of it. We detail how, where and in what form we integrated the materials into our course. We describe assignments that are based on the materials from the competition as well as additional materials we incorporated into our course. We finish by evaluating our experience in teaching with the materials as well as summarize relevant student feedback. We make recommendations for those who wish to adopt the materials.
 
 

Pedagogical possibilities for the dice game pig

Simple examples are teaching treasures. Finding a concise, effective illustration is like finding a precious gem. When such an example is fun and intriguing, it is educational gold. In this paper, we share the jeopardy dice game of Pig, which has extremely simple rules, engaging play, and a complex optimal policy. We describe its historical uses in mathematics, and share ways in which we have used the game to teach basic concepts in CS1, and intermediate concepts in introductory artificial intelligence, networking, and scientific visualization courses. We also describe the rich challenges Pig offers for undergraduate research in machine learning.

Scaffolding in teaching knowledge representation

Knowledge representation is one of the most difficult tasks for students in introductory Artificial Intelligence courses. We study the impact of varying levels and types of scaffolding in teaching ...

Wumpus World in introductory artificial intelligence

With an emphasis toward hands-on learning of Artificial Intelligence (AI) concepts, we present an overview of an introductory AI course that uses a series of five projects based in the game of Wump...

MLeXAI

Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that can be closely integrated into a one-term AI course. Each project involves the development of a machine learning system in a specific application. These projects have been used in six different offerings over a three-year period at three different types of institutions. While we have presented a sample of the projects as well as limited preliminary experiences in other venues, this article presents the first assessment of our work over an extended period of three years. Results of assessment show that the projects were well received by the students. By using projects involving real-world applications we provided additional motivation for students. While illustrating core concepts, the projects introduced students to an important area in computer science, machine learning, thus motivating further study.

Teaching Introductory Artificial Intelligence with Pac-Man

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.

Creating significant learning experiences in introductory artificial intelligence

We introduced an arcade-style gaming environment for use in a mixed undergraduate and graduate introductory artificial intelligence (AI) course. Our primary goal in this course was to provide students with a "significant learning experience" [3]. We achieved this goal by creating projects based in the game environment that illustrate several major AI topic areas. These projects were designed to be challenging, enjoyable, and to demonstrate AI programming in a realistic environment. Each of the projects was designed to be feasible for all the students yet flexible enough to allow the stronger students to explore alternative solutions. We evaluated our success in achieving these goals through student evaluations, comments, and exam grades.

Teaching AI through machine learning projects

An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. It is generally recognized that an introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach [9]. Others have been working to integrate Robotics into the AI course [1, 2, 3].We present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence course around the theme of machine learning. This involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning also provides a bridge between AI technology and modern software engineering. In his article, Mitchell discusses the increasingly important role that machine learning plays in the software world and identifies three important areas: data mining, difficult-to-program applications, and customized software applications [6].We have developed a suite of adaptable, hands-on laboratory projects that can be closely integrated into the introductory AI course. Each project involves the design and implementation of a learning system which will enhance a particular commonly-deployed application. The goal is to enhance the student learning experience in the introductory artificial intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the important relationship between AI and computer science in general, and software development in particular. Details on this project as well as samples of course materials developed are published in [4, 5, 7, 8] and are available at the project website at http://uhaweb.hartford.edu/compsci/ccli.We present an overview of our work along with a detailed presentation of one of these projects and how it meets our goals.The project involves the development of a learning system for web document classification. Students investigate the process of classifying hypertext documents, called tagging, and apply machine learning techniques and data mining tools for automatic tagging. Our experiences using the projects are also presented.

Poker as a group project for artificial intelligence

Assigning the development of a poker-playing agent as a group project allows flexibility with respect to the topics and techniques typically covered in an introductory Artificial Intelligence course. A poker agent project also provides students the experience of 'authentic' AI research, due to the status of poker as an 'unsolved' problem in AI. Despite this status, a poker agent project is feasible for a semester, half-semester, or quarter-long group project. Problems in assigning group projects are also considered, as are suggestions for mitigating these problems.

Teaching Introductory Artificial Intelligence Using a Simple Agent Framework

This paper describes a flexible method of teaching introductory artificial intelligence (AI) using a novel, Java-implemented, simple agent framework developed specifically for the purposes of this course. Although numerous agent frameworks have been proposed in the vast body of literature, none of these available frameworks proved to be simple enough to be used by first-year students of computer science. Hence, the authors set out to create a novel framework that would be suitable for the aims of the course, for the level of computing skills of the intended group of students, and for the size of this group of students. The content of the introductory AI course in question is a set of assignments that requires the students to use intelligent agents and other AI techniques to monitor, filter, and retrieve relevant information from the World Wide Web. It represents, therefore, a synthesis of the traditional objectivist approach and a real-world-oriented, constructivist approach to teaching programming to novices. The main aim of implementing such a pedagogy was to engage the students in learning to which they personally relate while attaining intellectual rigor. Classroom experience indicates that students learn more effectively when the traditional objectivist approach is combined with a constructivist approach than when this orthodox approach to teaching programming to novices is used alone.