Generative artificial intelligence (GenAI) is emerging as a transformative technology in higher education, particularly in programming instruction. However, its impact on learning, motivation, and the educational environment must still be fully understood. This study aims to determine the capacity of GenAI to generate effective computer programming learning in STEM university students, comparing it with active learning methods based on video. An experiment was conducted with 40 computer engineering students divided into two groups: one using GenAI (Google Gemini 1.5) and another employing educational videos. Pre- and post-tests of knowledge and the Intrinsic Motivation Inventory (IMI) were applied to evaluate learning, intrinsic motivation, and the learning environment. No significant differences in learning were found between the groups. However, GenAI significantly increased perceived autonomy and reduced perceived effort and pressure, while video-based learning significantly improved perceived competence. These findings suggest that both methods seem to motivate in diverse ways and that they could complement each other in an integrated teaching approach, offering new perspectives for designing programming learning environments in higher education.

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this research work, we have designed a mechanism to generate artificial somatic punishments in an autonomous system. An autonomous system is understood as a system capable of performing autonomous behavior and decision making. We incorporated this mechanism within a decision model oriented to support decision making on stock markets. Our model focuses on using artificial somatic punishments as a tool to guide the decisions of an autonomous system. To validate our proposal, we defined an experimental scenario using official data from Standard & Poor’s 500 and the Dow Jones index, in which we evaluated the decisions made by the autonomous system based on artificial somatic punishments in a general investment process using 10,000 independent iterations. In the investment process, the autonomous system applied an active investment strategy combined with an artificial somatic index. The results show that this autonomous system presented a higher level of investment decision effectiveness, understood as the achievement of greater wealth over time, as measured by profitability, utility, and Sharpe Ratio indicators, relative to an industry benchmark.

Sex differences have been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to sex and the conditions that generate these effects. The present work studied the effects of a drill-and-practice mathematical game on primary students. The study focused on an analysis by sex, measuring motivation and learning in the practice activity. Also, two instructional mechanics were considered regarding the question answering to search for possible differences: a multiple-try feedback (MTF) condition and a single-try feedback (STF) condition. A total of 81 students from four courses and two schools participated in the intervention. The study’s main findings were as follows: (a) the girls outperformed the boys in terms of the students’ learning gains; (b) the girls presented lower levels of competence and autonomy than the boys; (c) under MTF, the girls presented lower levels of autonomy but no differences in competence contrasted with the boys; (d) under STF, the girls presented lower levels of competence but no differences in autonomy contrasted with the boys; (e) no sex differences existed in interest, effort, and value, in general, as per the instructional condition. This study enhances the knowledge of sex differences under diverse instructional settings, in particular providing insights into the possible differences by sex when varying the number of attempts provided to students.

Gender has been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to gender and the conditions that generate these effects. The present work studies the effects of a drill-and-practice mathe-matical game on primary students. The study focused on an analysis by gender, measuring learning performance, and motivation in the practice activity. Also, two instructional mechanics were considered regarding the question answering, a multiple-try (MTF) and a single-try (STF) condition, to search for possible differences. A total of 81 students from 4 courses and 2 schools participated in the experiment. The study's main findings were that: (a) the girls outperformed boys in terms of students' learning gains, (b) girls presented lower levels of competence and autonomy than boys, (c) in the MTF, girls presented lower levels of autonomy but no differences in competence contrasted to boys, (d) in the STF, girls presented lower levels of competence but no differences in autonomy against boys, and (e) no differences existed in interest, effort and value among gender overall o per instructional condition. This study enhances the knowledge of gender differences under diverse instructional settings, in particular providing insights into possible differences by gender when varying the number of attempts provided to students.

The number of attempts to provide students is a key instructional characteristic in computer-based learning (CBL). However, it has not been covered extensively, and there is a need to delve deeper into the factors affecting multiple-try performance and allowing its successful use, including the learner’s involved emotional processes. This study examines the effects of multiple-try on a drill-and-practice mathematical game devoted to primary school students. A total of 73 students from four courses from two schools participated in the experiment. They were randomly assigned to a 3-attempt multiple-try (MTF) and a single-try knowledge of correct response (KCR) conditions. The study covered impacts on learning performance, together with motivation, effort, pressure, and the value of students regarding the learning activity based on the self-determination theory (SDT) perspective and its cognitive evaluation sub-theory (CET). The study’s main findings were that (a) the MTF condition outperformed KCR in terms of students’ learning gains; (b) MTF presented higher levels of perceived competence and autonomy, which, according to SDT, fosters motivation and learning; (c) a cost was yielded in students’ perceived pressure under MTF; and (d) perceived effort and value was similarly high for both conditions despite learning differences. This study complements the existing literature on multiple-try, providing insights into what conditions are beneficial for multiple-try use.

This work analyzes the implementation of an artificial mechanism inspired by a biological somatic marker that ables a passenger agent to both, react to changes in the service, as well as keep said reactions as a memory for future decisions. An artificial mental model was designed, and the passenger agent was implemented as an autonomous decision-making system, where both, the choice of the transport operator and the evaluation of the received service were fully delegated to the system. The evaluation of the service experience is not only based on rational aspects (such as the cost of the trip) but also on subjective aspects related to the satisfaction level derived from the passenger's experience. The experimental scenario considered 10,000 trip requests simulated within an artificial map that emulates characteristics that are usually present in a city, such as vehicular congestion, the unsafety of certain streets, or the benefits of an area with tourist interest. The results show that the option to travel under a transport operator with a touristic profile is a trend. Unlike current cases in the industry, this research work explores the scenario where the passenger can have as a client a trip profile with memory, differentiated from other clients, and can receive more than one trip proposal for the same trip request, according to the different conditions that the passenger is looking for.

Flexible passenger transportation occurs in response to the rigidity of the routes and travel conditions offered by conventional public transportation systems. In this modality, both origin and destination are explicitly indicated by the passenger. Usually, the definition of travel routes is oriented towards operational efficiency, which is not necessarily related to the passenger’s travel experience. Therefore, the question arises regarding the extent of flexibility of passenger transportation solutions currently available in the industry. To the best of our knowledge, no proposals have incorporated artificial emotions in cognitive agents applied to the domain of flexible passenger transportation, allowing these agents to autonomously deliberate and decide after consideration of objective (travel time or cost) and subjective variables (emotions and satisfaction) within a unique integrated layer, with the help of passenger and travel independent profiles. This work considers a single hypothetical city map based on the use of a matrix of dimensions 500 × 500, which has congested, touristic, unsafe, and neutral streets. Three types of artificial agents were defined: Passenger Agent, Vehicle Agent, and Fleet Management Agent, with specific functions performed by each. To select a proposal, the Passenger Agent determines a selectivity index from the previous calculation of three metrics: travel time, travel cost, and utility. By considering thirty different experimental scenarios, each one executed 1000 times independently, it is possible to observe promising results that demonstrate high utility for the passenger in some cases, while the travel time and cost do not exceed the average of the system.

In the literature, several approaches have been proposed to integrate and optimize product supply and construction processes associated with demand management. However, in Industry 4.0, there needs to be more studies related to applying techniques that directly affect the programming and reprogramming process that integrates the industries at the operational level. This document proposes a flow-shop scheduling procedure to address the problem of planning the repair of medical equipment in public hospitals whose main objective is to eliminate downtime and minimize total production time. The research stems from the practical problem of responding to clinical users who make use of critical equipment, such as mechanical respirators, due to COVID-19, and the limited quantity of this equipment, which makes it necessary to have repair planning processes that seek to keep the equipment in operation for the most extended amount of time. The novelty of this study is that it was applied to a critical and real problem in the industry with a high economic and social impact, which has not been explored previously. The results show improvements in the overall planning and execution of electro-medical equipment repair. Several improvements to the applied methods were identified as future work, such as the need to consider work interruptions and psychosocial effects on workers due to the stricter planning of execution times.

This work presents the design of an adaptive, intelligent, autonomous system based on the use of artificial somatic markers and personality traits of the Big Five model. The aim is for the system to be capable of performing decision-making processes autonomously without human intervention, adapting its decision strategy according to the domain conditions and obtained results. For this, the system permanently has one of the five different personality profiles of the Big Five model, and the ability to adapt its personality profile in real time based on artificial somatic activation, that is, to the reactions experienced by the system in the face of perceived stimuli during the autonomous decision-making process. The novelties of this work include the following: design of a general operational framework of an adaptive, intelligent, autonomous system, which integrates domain indicators, artificial somatic markers, personality profiles, domain profiles, and business rules; design of a somatic index function; design of a scheme for the translation of artificial somatic reactions to personality profiles of the Big Five model; and definition of a case study based on stock markets. The promising results show that the system achieves effectiveness and efficiency from the decisions made.

This work analyzes the route time, the service cost, and well-being in flexible passenger transportation. By using affective intelligent agents within an artificial environment, seven different scenarios were defined. To offer a broader view, three profiles of passenger agents were considered in each of the seven previously mentioned scenarios: Student profile, which assesses the cost of the route over other decision criteria; Worker profile, which values the time of the route over other factors; and Tourist profile, which values the well-being derived from the route over other factors. Well-being is understood as the satisfaction perceived by the passenger derived from the transport service. Likewise, the transit through streets with positive or negative memories has a direct effect on the well-being of the passenger agent. Depending on the scenario and the configuration of parameters, the results show that in some cases obtaining higher well-being does not imply a substantially higher cost. Likewise, in some cases, following an option that seeks higher well-being does not substantially represent a longer travel time.

Sometimes, the conscious act of decision-making in humans is dramatically interrupted by situations that warrant an immediate response (e.g. when there is an imminent risk). The human body somatizes this interruption such that an action could be taken without a rational analysis. The above is known as a somatic marker. According to the somatic marker hypothesis, somatic markers could directly influence several ambits of decision-making. This research work presents the incorporation of artificial somatic reactions into affective autonomous agents who implement decision-making in the stock market. This implies the design of a general decision architecture for stock markets considering artificial somatic reactions and the definition of a set of decision-making algorithms for supporting investment decisions performed by affective autonomous agents (considering artificial somatic reactions). Test scenarios were defined using official data from Standard & Poor's 500 and Dow Jones. The experimental results are promising and indicated that affective autonomous agents are able to experience artificial somatic reactions and achieve effectiveness and efficiency in their decision-making.

The somatic marker hypothesis proposes that when a person faces a decision scenario, many thoughts arise and different “physical consequences” are fleetingly observable. It is generally accepted that affective dimension influences cognitive capacities. Several proposals for including affectivity within artificial systems have been presented. However, to the best of our knowledge, a proposal that considers the incorporation of artificial somatic markers in a disaggregated and specialized way for the different phases that make up a decision-making process has not been observed yet. Thus, this research work proposes a framework that considers the incorporation of artificial somatic markers in different phases of the decision-making of autonomous agents: recognition of decision point; determination of the courses of action; analysis of decision options; decision selection and performing; memory management. Additionally, a unified decision-making process and a general architecture for autonomous agents are presented. This proposal offers a qualitative perspective following an approach of grounded theory, which is suggested when existing theories or models cannot fully explain or understand a phenomenon or circumstance under study. This research work represents a novel contribution to the body of knowledge in guiding the incorporation of this biological concept in artificial terms within autonomous agents.

This paper presents the design of an artificial autonomous system (called AAS) for the stock market domain that considers an approximation from the Big Five model, which proposes that the personality of an individual belongs to one of five different personality profiles: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several studies have explored investment and financial issues while considering the Big Five model, usually by analyzing data obtained from surveys applied to real people. However, to the best of our knowledge, there are no proposals that suggest the design of an AAS for supporting investment decisions that use the Big Five model as the central approach. The main objective of this proposal is to design an AAS for making investment decisions, where the decisions are adjusted to market conditions through the use of a policy function that adapts over time. This policy function adjusts the consumption level and investment portfolio composition required by the investment profile, considering both the market conditions and the Big Five model profile associated with the AAS. The effectiveness of the investment process is measured by observing the variations in the accumulated wealth and utility. The utility is measured through an abstract representation of the well-being or satisfaction of the investor (i.e., the AAS). AAS-Extraversion obtained the highest accumulated wealth, while AAS-Agreeableness obtained the highest level of utility, showing that the accumulated wealth is only one factor influencing the investor's well-being.

SummarySoftware design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod.

This paper presents the design of a resilience mechanism for supporting investment decision-making processes performed by artificial autonomous systems. In the field of Psychology, resilience is understood as the capacity of people to overcome adversity. Resilience has been determined to be a permanent necessary element for the life of an individual. In addition, different levels of intelligence, analysis capacities, and degrees of autonomy have been progressively incorporated within information systems that are oriented to support decision-making processes, such as those for stock markets. Particularly, the inclusion of affective criteria or variables within decision-making systems represents a promising line of action. However, to the best of our knowledge, there are no proposals that suggest the inclusion of a psychological approach to resilience within an autonomous decision-making system for stock markets. Specifically, the incorporation of a psychological approach to resilience allows the autonomous system to face special difficult investment scenarios (e.g., an economic shock) and prevent the system from achieving a permanent negative performance. Thus, psychological resilience can enable an artificial autonomous system to adapt its decision-making processes according to uncertain investment environments. Our proposal conducts experiments using official data from the Standard & Poor's 500 Index. The results are promising and are based on a second-order autoregressive model. The test results suggest that the use of a resilience mechanism within an artificial autonomous system can contain and recover the affective dimensions of the system when it faces adverse decision scenarios.

This paper presents the design of an affective algorithm for implementing autonomous decisionmaking systems that incorporate an emotional stabilizer mechanism for the use in the stock market domain. Emotions have a direct influence on human decision-making processes. Non-deterministic behavior in humans can be partially explained by emotions. In this sense, an artificial emotion can be implemented as a synthetic abstraction derived from the observation of human emotions. This paper presents studies related to emotional stability and emotional regulation. However, to the best of our knowledge, it is not possible to identify studies that define a relationship between the regulation of artificial emotions and the decision effectiveness of autonomous decision-making systems, specifically for the stock market domain. With the aim to improve investment results in the stock market domain, a mechanism based on artificial emotions is presented that was designed as a single layer of decision criteria defined by both rational and emotional perspectives. Along with the proposal of an emotional stabilizer mechanism, different values of emotional bandwidths and emotional update rates were tested, aiming to explore the degree of influence of these parameters on the effectiveness of investment decisions made by artificial investors. Our proposal considers the definition of an experimental scenario based on official data from the New York Stock Exchange. The results are promising and include a linear regression analysis. The test results suggest that the use of autonomous affective decision-making systems with emotional stabilization can improve the effectiveness of the decision made.

This research presents a proposal of malware classification and its update based on capacity and obfuscation. This article is an extension of [4]a, and describes the procedure for malware updating, that is, to take obsolete malware that is already detectable by antiviruses, update it through obfuscation techniques and thus making it undetectable again. As the updating of malware is generally performed manually, an automatic solution is presented together with a comparison from the standpoint of cost and processing time. The automated method proved to be more reliable, fast and less intensive in the use of resources, specially in terms of antivirus analysis and malware functionality checking times.

This paper proposes to solve planning repair of medical equipment in a hospital in Chile based in the planning type Flow-Shop Scheduling Problem, with the objective to eliminate downtime and minimize the total production time. To deliver a solution with a final makespan as low as possible was used the Tabu Search method. The proposal was tested using a real case study planning and execution of repairs, and the results are promising, since obtaining initial solutions using Tabu Search generated very low planning and time to implement them was feasible to achieve the estimated time.

Hyper-heuristics are optimization techniques for solving hard combinatorial problems. Their main feature is that their design involves an important decoupling of the search components from the problem domain ones. This allows them to extend their applicability to different problem domains without major redesign, unlike traditional methods such as metaheuristics. In this work, a hyper-heuristic is evaluated for a transportation problem. The implemented hyper-heuristic uses a greedy operator, and it implements an adapter layer that would allow it to be used in other similar problems. Experimental results shows balanced solution quality and CPU time performance, regarding other metaheuristics in literature.

This work proposes the use of a constructive heuristic called Shifting Bottleneck Procedure for solving the Job-Shop Scheduling problem, with the aim to minimize the makespan, that is, the final delay of production planning. Because the constructive heuristic delivers an initial solution, then a Tabu Search method was used, with the aim to obtain a planification with the lowest possible makespan. The proposal was tested using benchmark data, and the results are promising, because the obtention of initial solutions using a constructive heuristic allows to decrease the processing time, and usually, the final results obtained from Tabu Search method are better.