This research focused on a formal (theory based) approach to designing Intelligent Tutoring System (ITS) authoring tool involving two specific conventional pedagogical theories—Conversation Theory (CT) and Cognitive Apprenticeship (CA). The research conceptualised an Augmented Conversation and Cognitive Apprenticeship Metamodel (ACCAM) based on apriori theoretical knowledge and assumptions of its underlying theories. ACCAM was implemented in an Intelligent Learning Activity Builder System (ILABS)—an ITS authoring tool. ACCAM’s implementation aims to facilitate formally designed tutoring systems, hence, ILABS―the practical implementation of ACCAM― constructs metamodels for Intelligent Learning Activity Tools (ILATs) in a numerical problem-solving context (focusing on the construction of procedural knowledge in applied numerical disciplines). Also, an Intelligent Learning Activity Management System (ILAMS), although not the focus of this research, was developed as a launchpad for ILATs constructed and to administer learning activities. Hence, ACCAM and ILABS constitute the conceptual and practical contributions that respectively flow from this research. ACCAM’s implementation was tested through the evaluation of ILABS and ILATs within an applied numerical domain―the accounting domain. The evaluation focused on the key constructs of ACCAM―cognitive visibility and conversation, implemented through a tutoring strategy employing Process Monitoring (PM). PM augments conversation within a cognitive apprenticeship framework; it aims to improve the visibility of the cognitive process of a learner and infers intelligence in tutoring systems. PM was implemented via an interface that attempts to bring learner’s thought process to the surface. This approach contrasted with previous studies that adopted standard Artificial Intelligence (AI) based inference techniques. The interface-based PM extends the existing CT and CA work. The strategy (i.e. interface-based PM) makes available a new tutoring approach that aimed fine-grain (or step-wise) feedbacks, unlike the goal-oriented feedbacks of model-tracing. The impact of PM—as a preventive strategy (or intervention) and to aid diagnosis of learners’ cognitive process—was investigated in relation to other constructs from the literature (such as detection of misconception, feedback generation and perceived learning effectiveness). Thus, the conceptualisation and implementation of PM via an interface also contributes to knowledge and practice. The evaluation of the ACCAM-based design approach and investigation of the above mentioned constructs were undertaken through users’ reaction/perception to ILABS and ILAT. This involved, principally, quantitative approach. However, a qualitative approach was also utilised to gain deeper insight. Findings from the evaluation supports the formal (theory based) design approach—the design of ILABS through interaction with ACCAM. Empirical data revealed the presence of conversation and cognitive visibility constructs in ILATs, which were determined through its behaviour during the learning process. This research identified some other theoretical elements (e.g. motivation, reflection, remediation, evaluation, etc.) that possibly play out in a learning process. This clarifies key conceptual variables that should be considered when constructing tutoring systems for applied numerical disciplines (e.g. accounting, engineering). Also, the research revealed that PM enhances the detection of a learner’s misconception and feedback generation. Nevertheless, qualitative data revealed that frequent feedbacks due to the implementation of PM could be obstructive to thought process at advance stage of learning. Thus, PM implementations should also include delayed diagnosis, especially for advance learners who prefer to have it on request. Despite that, current implementation allows users to turn PM off, thereby using alternative learning route. Overall, the research revealed that the implementation of interface-based PM (i.e. conversation and cognitive visibility) improved the visibility of learner’s cognitive process, and this in turn enhanced learning—as perceived.

Fomentar la lectura se ha convertido en uno de los desafíos primordiales en el ámbito educativo, y en este contexto se identificó deficiencia en comprensión lectora, teniendo como objetivo general implementar un Sistema Tutor Inteligente (STI) para fortalecer el aprendizaje de comprensión lectora en el segundo grado de primaria en la I.E 11050 Rómulo Cabrera Chilcon. El STI se desarrolló siguiendo el marco de trabajo SCRUM y la metodología CRISP DM. De acuerdo con los resultados el STI es un sistema especializado, personalizado que clasifica el nivel de aprendizaje en el que se encuentra cada estudiante y así mismo enviándoles a un módulo diferente (nivel de inicio, proceso, satisfactorio), también se aplicó el modelo Alicia y modelo de evaluación ECE, la cual ayudó a identificar en qué nivel requiere mejoras y así mismo brinda opciones de retroalimentación. El STI, cuenta con un chatbot, donde se empleó el modelo idóneo de redes neuronales artificiales multicapa, la cual permite que el chatbot interactúe con el estudiante brindándole definiciones y significado de una palabra que desconozca, el chatbot puede proporcionar definiciones y significados según Real Academia Española (RAE), para ayudar a comprender mejor el texto. Finalmente, el STI fue validado a través de juicios de expertos, brindando un 98 % de grado de satisfacción considerando la característica de adecuación funcional de la norma IEC 25010: 2015. / Promoting reading has become one of the primary challenges in the educational field, and in this context a deficiency in reading comprehension was identified, with the general objective of implementing an Intelligent Tutor System (STI) to strengthen the learning of reading 6 comprehension in the second. Primary grade at I.E 11050 Rómulo Cabrera Chilcon. The STI was developed following the SCRUM and CRISP DM methodologies. According to the results, the STI is a specialized, personalized system that classifies the level of learning in which each student is and also sending them to a different module (beginning level, process, satisfactory), the Alicia model was also applied, which helped identify at what level requires improvement and also provides feedback options. The STI has a chat-bot, where the ideal model of multilayer artificial neural networks was used, which allows the chat bot to interact with the student by providing definitions and meanings of a word that they do not know, the chatbot can provide definitions and meanings According to the Royal Spanish Academy (RAE), to help better understand the text. Finally, the STI was validated through expert judgments, providing a 98% degree of satisfaction considering the functional adequacy characteristic of the IEC 25010: 2015 standard.