Edufit – Career Catalyst: AI-Powered Resume Analysis and Adaptive Learning for Career Development

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced Transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability.

This study investigated the impact of AI integration, specifically ChatGPT, on personalized learning involving 785 college students in the Philippines who took the online survey. Utilizing regression analysis and an Omnibus ANOVA test, the study examined the influence of AI Integration alongside demographic variables such as age, sex, educational level, and type of school on personalized learning. Results indicate that AI integration can explain a substantial portion of the variability in personalized learning outcomes (approximately 88.54%). Specifically, ChatGPT demonstrates a significant positive effect on personalized learning, suggesting that as ChatGPT integration increases, personalized learning experiences also increase. However, demographic variables such as age, sex, educational level, and type of school show minimal effects on personalized learning outcomes, except for a potential trend for higher scores in private universities and colleges compared to state universities and colleges. These findings underscore the pivotal role of AI technologies, like ChatGPT, in enhancing personalized learning experiences while highlighting the need for further exploration of contextual factors influencing educational outcomes. The implications extend beyond the study to offer insights for educational stakeholders and policymakers, emphasizing the potential benefits of AI-driven personalized learning initiatives. However, limitations such as sample characteristics, measurement bias, and technology accessibility should be addressed in future research endeavors to maximize the benefits of AI integration in education.

Despite recent advances in technology, personalized learning to address diverse needs of students remains difficult to achieve at scale. With the availability and affordability of smart devices in the era of the Internet of Things, learners, parents, and educators are more “connected” than ever before. Education stakeholders and technology developers can leverage these advances to collect data about, inform, deliver, and improve education for all learners. In this paper, we review the core components of a Smart Learning framework and describe a personalized mastery-based learning system that leverages the framework to deliver personalized learning at scale. In the context of Smart Learning in the Internet of Things, we propose an Ambient and Pervasive Personalized Learning Ecosystem (APPLE), a learner-centered approach that uses Bloom’s Four Agents of Change in the Internet of Things ecosystem to provide learners a comprehensive and personalized learning experience. This ecosystem uses people, processes, data, things, and networked connections to create new capabilities, richer learning experiences, and unprecedented educational opportunities for learners, educators, and families. We further discuss the challenges surrounding the implementation of such an ecosystem, specifically calling for applications of learning engineering approaches, the need of interoperability across systems and components, and the importance of ethical considerations.

One of the biggest obstacles faced by user command based anomaly detection techniques is the paucity of data. Gathering command data is a slow process often spanning months or years. In this paper, we propose an approach for data generation based on customizable templates, where each template represents a particular user profile. These templates can either be user-defined or created from known data sets. We have developed an automated tool called RACOON, which rapidly generates large amounts of user command data from a given template. We demonstrate that our technique can produce realistic data by showing that it passes several statistical similarity tests with real data. Our approach offers significant advantages over passive data collection in terms of being nonintrusive and enabling rapid generation of site-specific data. Finally, we report the benchmark results of some well-known algorithms against an original data set and a generated data set.

Learner-centered education has the potential to meet the needs of individual students and better prepare them for a rapidly changing global world. However, it can be a challenging task to implement learner-centered education in the current education system which was designed for sorting rather than learning. Although an increasing number of research studies report that teachers have positive attitudes toward learner-centered education, there is a paucity of research that has examined teachers’ learner-centered practice. To address this gap, this study examined the strategies and tools used by 125 teachers to create learner-centered classrooms using an online survey. Further, the study explored the barriers they faced when using technology to facilitate learner-centered instruction. The strategies and tools used for learner-centered instruction are reported in six major categories: (1) getting to know individual students, (2) building a positive and supportive culture, (3) providing personalized learning experiences, (4) providing authentic learning experiences, (5) facilitating collaborative learning, and (6) facilitating self-regulated learning. The major barriers to using technology to support learner-centered pedagogy included lack of time, lack of technology, lack of knowledge of learner-centered instruction, and standardized tests.

Recent research has revealed benefits to using technology to personalize and adapt a learning experience to the learner’s preferences, habits, and developing expertise (Cardona et al. in 2013 IEEE Colombian conference on communications and computing (COLCOM), pp. 1–6, 2013; Johnson and Samora in Glob Educ J 2016; Murray and Perez in Informing Sci: Int J Emerg Transdiscipline 18, 2015; Yang et al. in Educ Technol Soc 16, 2013). Personalized learning can give each learner the opportunity to learn effectively and efficiently based on his or her own assets of skills, knowledge, and abilities, supporting a student-centered pedagogy. It also can provide the opportunity for wide-scale access to quality learning and teaching where public or open education has fallen short due to limited resources (Dockterman in npj Sci Learn 3:15, 2018). While there are a number of studies of personalized learning design and implementation, most of these studies were conducted in K-12 classroom environments, where the approach to teaching and learning is different than the approach for adult learners in workplace or informal learning environments. Adult learners have various types and amounts of reasons, access, time, motivation, and resources for learning, which would affect the way a tailored or individualized experience would be designed for them as compared to children (Knowles et al. in The adult learner, 2012). This chapter will review existing research on personalized learning and will define andragogy, which is teaching and learning designed for adult learners. Recommendations for designing personalized learning experiences and technologies will be explored, such as designing proper diagnostics, scaffolds, and features for higher-order thinking skills development. Based on this literature review and outline of recommended practices, the author will delineate next steps and considerations for future research on personalized learning for adults.

This study investigates the potential of artificial intelligence (AI) to revolutionize personalized learning by developing and empirically evaluating the Adaptive Personalization Theory of Learning (APT) model. The APT paradigm uses AI-powered personalized learning algorithms, real-time adaptive assessments, learner engagement strategies, cognitive scaffolding, and ethical safeguards to provide flexible, personalized learning experiences. The study confirms the model's constructs through qualitative methods such as case studies, interviews, and classroom observations. It illustrates how AI improves learning outcomes by continuously tailoring content and evaluations to individual learner needs. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. However, the study also identifies ethical concerns, such as potential biases in AI algorithms, emphasizing the significance of establishing transparent, fair systems. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research, emphasizing long-term impacts, ethical frameworks, and practical implementation tactics, and establishing the APT model as a significant contribution to AI in education.

The personalized recommender system, which is extensively used in many domains, is suggested as a solution to the information overload issue.Recently, and with great speed, the area of job recommender systems for recruiting has arisen.The user profiles and recommendation technologies of the job recommender system have attracted attention, have undergone academic study, and have been implemented for various use cases in industries.. Based on the available research, we present some fundamental user profile concepts as well as some popular recommendation technologies in this paper.Across many areas, adaptive recommender systems are becoming more widespread.Regardless of the domain, personalized recommendations are a great method to enhance user experience, but they must be implemented properly within the interaction context.By limiting the search area to items associated with the user's profile and alerting the user to those items most likely to suit their interests, adaptive recommender systems basically give users a more intelligent way to traverse and explore complex information landscapes.Customized recommender systems have recently piqued the interest of firms that serve job seekers.

Recent research has utilized resumes and occupational profiles to identify and quantify discrimination within conversational AI systems. We extend this concept by proposing a novel approach to quantification which leverages Holland’s theory of occupational interests to provide a more holistic interpretation of discrimination in these systems. Building upon the seminal work of Bertrand and Mullainathan (Am Econ Rev 94(4):991–1013, 2004), which examined racial discrimination in hiring through a naturalistic experiment, we adapt their methodology to explore biases embedded in AI-driven job recommendations. By manipulating gender and race within user profiles, as well as investigating if these characteristics are inferred by name alone, we assess the extent to which conversational AI models (GPT-3.5, GPT-4, Llama 2, and Gemini) exhibit discriminatory patterns. Our findings indicate significant disparities in job recommendations based on demographic factors. These findings vary between AI models. We extend previous research in this domain which explores bias with singular metrics by transforming recommendations via Holland’s theory to a multidimensional set of characteristics allowing for a more thorough interpretation of bias. Our work underscores the importance of continuous auditing and refinement of AI systems to mitigate bias and promote fairness in automated decision-making processes.

Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings everyday, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever. While recommendation systems are successfully advancing in variety of online domains by creating social and commercial value, the job recommendation domain is less explored. Existing systems are mostly focused on content analysis of resumes and job descriptions, relying heavily on the accuracy and coverage of the semantic analysis and modeling of the content in which case, they end up usually suffering from rigidity and the lack of implicit semantic relations that are uncovered from users' behavior and could be captured by Collaborative Filtering (CF) methods. Few works which utilize CF do not address the scalability challenges of real-world systems and the problem of cold-start. In this paper, we propose a scalable item-based recommendation system for online job recommendations. Our approach overcomes the major challenges of sparsity and scalability by leveraging a directed graph of jobs connected by multi-edges representing various behavioral and contextual similarity signals. The short lived nature of the items (jobs) in the system and the rapid rate in which new users and jobs enter the system make the cold-start a serious problem hindering CF methods. We address this problem by harnessing the power of deep learning in addition to user behavior to serve hybrid recommendations. Our technique has been leveraged by CareerBuilder.com which is one of the largest job boards in the world to generate high-quality recommendations for millions of users.

<p><span style="font-size: medium;"><span style="font-family: Times New Roman;">Personalization is a good supplement for learning process. It has been claimed that personalization has a huge potential of providing solution to facilitate the learning path based on children ability and preferences. Diverse research on personalized learning for children have been conducted which, are commonly concerns on the development and implementation of personalized learning products and services. However these researches have little emphasized in exploring slow learner personalized learning process particularly on their reading ability. With that, this paper aims to highlight two key important processes of personalization for slow learner children which are construction of user profile and scenario. The scope of this study is on personalization of reading for slow learner children. There were 13 slow learner children with reading difficulties from primary school participated in this study. The key findings from this study are the construction of user profile and scenario that represent the personalization for reading. These user profile and scenario construction then provide guidelines for the development of personalized interface design for slow learner reading application. </span></span></p><p><strong> </strong></p><p> </p>

Nowadays, many researchers focus on developing learning systems with personalized learning mechanisms to adaptively provide learning paths in order to promote the learning performance of individual learner. Meanwhile, finding a suitable learning path has become a crucial issue for learners who want to learn new things quickly and effectively. We propose a personalized learning path recommender in this paper, which can recommend learning materials of every step in the learning process of a learner. as we all known, the performance of a recommender system depends on the accuracy of the user profiles used to represent the characteristics of the users. We firstly make advantage of social tags to construct user profiles. We consider that the knowledge units in the learning path have precedence relationship. then we make use of Bayes formula to predict the probability of the next learning materials within mostly similar learners. the Experiments show that our method is practical and effective.

Most recommender systems, such as collaborative filtering, cannot provide personalized recommendations until a user profile has been created. This is known as the new user cold-start problem. Several systems try to learn the new users' profiles as part of the sign up process by asking them to provide feedback regarding several items. We present a new, anytime preferences elicitation method that uses the idea of pairwise comparison between items. Our method uses a lazy decision tree, with pairwise comparisons at the decision nodes. Based on the user's response to a certain comparison, we select on-the-fly what pairwise comparison should next be asked. A comparative field study has been conducted to examine the suitability of the proposed method for eliciting the user's initial profile. The results indicate that the proposed pairwise approach provides more accurate recommendations than existing methods and requires less effort when signing up newcomers.

Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model

The study aimed to assess the impact of media technology on the teaching and learning process of Islamic religious education within an independent curriculum. Through interviews, observations, and document analysis, the study found that integrating media technology positively influenced the teaching and learning experience. Multimedia presentations, educational websites, and digital applications enhanced students' engagement, motivation, and understanding of Islamic principles and teachings. Interactive and collaborative learning was facilitated, allowing students to participate actively in discussions and group activities. Media technology also provided opportunities for personalized learning, allowing students to explore Islamic concepts at their own pace and according to their learning styles. It fostered creativity and critical thinking skills by encouraging students to analyze and interpret religious texts through interactive media platforms. However, challenges such as limited access to technology resources, technical difficulties, and concerns about content authenticity were identified. The study emphasized the need for appropriate teacher training and support to integrate media technology into the curriculum effectively. In conclusion, the study demonstrated the effectiveness of media technology in Islamic Religious Education within an independent curriculum. It was integrating media tools enhanced engagement, motivation, and understanding among students while enabling interactive and personalized learning experiences. Addressing resources, technical issues, and content reliability challenges is essential for successful implementation. Keywords: Media Technology, Independent Curriculum, Interactive Learning