L’intelligenza artificiale (IA) si sta affermando quale strumento chiave per personalizzare l’apprendimento, come indicano la Commissione Europea (2023) e l’UNESCO (Miao & Cukurova, 2024). Tecnologie come tutor intelligenti, sistemi adattivi e dashboard stanno trasformando la didattica e ridefinendo il ruolo del docente (Tapalova & Zhiyenbayeva, 2022; Holmes et al., 2019; Hwang et al., 2020). Questo studio esplora la percezione degli insegnanti di 91 scuole italiane coinvolte nel progetto europeo AI4T (Artificial Intelligence for and by Teachers, Erasmus+), evidenziando sia interesse per le potenzialità dell’IA, sia accortezze legate alla sua applicazione. I dati rivelano opinioni favorevoli, ma anche cautele da parte degli insegnanti, offrendo una base empirica per comprendere il ruolo e l’impatto dell’IA nella scuola e nella personalizzazione dell’apprendimento.

This study explores the integration of artificial intelligence (AI) in Italian education, focusing on the perceptions of teachers involved in the European AI4T project. The analysis, based on a mixed approach, shows initial optimism towards the possibilities offered by AI, such as recognition of learning and automation of tasks. However, concerns also emerge about ethical issues such as privacy, responsibility for choices made and the impoverishment of interpersonal relationships. Despite the fact that AI is recognized as useful for simplifying administrative and teaching tasks, teachers consider it essential to maintain the human element in the educational process. The contribution invites reflection on the need for ethical regulation and ongoing literacy for responsible use of AI.

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops a new specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, the functional form and the model itself. The test is robust to the presence of heteroskedasticity of unknown form in the disturbances and the approach relates to the conditional moment test framework of Bierens ([1982, Journal of Econometrics 20, 105–134], [1990, Econometrica 58, 1443–1458]). The Bierens test is shown to be inconsistent in general against spatial alternatives and the new test introduces modifications to achieve test consistency in the spatial setting. A central element is the infinite-dimensional endogeneity induced by spatial linkages. This complexity is addressed by introducing a new component to the omnibus test that captures the effects of potential spatial matrix misspecification. With this modification, the approach leads to a simple pivotal test procedure with standard critical values that is the first test in the literature to have power against misspecifications in the spatial linkages. We derive the asymptotic distribution of the test under the null hypothesis of correct SAR specification and prove consistency. A Monte Carlo study is conducted to study its finite sample performance. An empirical illustration on the performance of the test in modeling tax competition in Finland is included.

We propose a computationally straightforward test for the linearity of a spatial interaction function. Such functions arise commonly, either as practitioner imposed specifications or due to optimizing behaviour by agents. Our conditional heteroskedasticity robust test is nonparametric, but based on the Lagrange Multiplier principle and reminiscent of the Ramsey RESET approach. This entails estimation only under the null hypothesis, which yields an easy to estimate linear spatial autoregressive model. Monte Carlo simulations show excellent size control and power. An empirical study with Finnish data illustrates the test's practical usefulness, shedding light on debates on the presence of tax competition among neighbouring municipalities.

Employing firm-level panel data from 2011 to 2015, we investigate the relationship between board diversity—in terms of gender and nationality—and outward foreign direct investment (OFDI) in Europe. Previous studies suggest that best-performing firms self-select into OFDI and that board diversity affects firm performance and strategic decisions. Controlling for endogeneity using instrumental variables and control functions, we find that firms with more diverse boards are less likely to open foreign subsidiaries. Furthermore, we explore the role of performance as modifier and mediator of the effect of board diversity on OFDI. Relying on suitable interaction terms, we show that the effect of board diversity on OFDI is stronger for more productive firms. We then decompose the effect of board diversity on OFDI into a negative direct effect and a positive performance-mediated effect, with the former outweighing the latter. Our findings are consistent with tough management monitoring by more diverse boards.

We provide in this paper asymptotic theory for a spatial autoregressive model (SAR, henceforth) in which the spatial coefficient, λ, is allowed to be less than or equal to unity, as well as consistent with a local to unit root (LUR) model and of the moderate integration (MI) from unity type, and the spatial weights are allowed to be similarity-based and data driven. Other special cases of our setting include the random walk, a model in which all the weights are equal, the standard SAR model in which λ<1 and the similarity based autoregression in which λ=1 and data do not display a natural order. As the norming rates for the asymptotic theory are very different in the λ<1 - compared with the λ=1 and LUR cases, we resort to random norming that treats all cases in a uniform manner. It turns out that standard CLT results prevail in a large class of models in which the infinity norm of the inverse of the weighting structure that characterizes the reduced-form process is Onγ , γ∈[0,1), and is non-standard in the case γ=1. We use a shifted profile likelihood to obtain results which are valid for all cases. A small simulation experiment supports our findings and the usefulness of our model is illustrated with an empirical application of the Boston housing data set in which the estimate of λ appeared to be very close to unity.

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, methods based on the quasi-likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference (II) approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the II procedure to take account of the parameterization of the variance–covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

In this article, we develop asymptotic theory for a spatial autoregressive (SAR) model where the network structure is defined according to a similarity-based weight matrix, in line with the similarity theory, which in turn has an axiomatic justification. We prove consistency of the quasi-maximum-likelihood estimator and derive its limit distribution. The contribution of this article is two-fold: on one hand, we incorporate a regression component in the data generating process while allowing the similarity structure to accommodate non-ordered data and by estimating explicitly the weight of the similarity, allowing it to be equal to unity. On the other hand, this work complements the literature on SAR models by adopting a data-driven weight matrix which depends on a finite set of parameters that have to be estimated. The spatial parameter, which corresponds to the weight of the similarity structure, is in turn allowed to take values at the boundary of the standard SAR parameter space. In addition, our setup accommodates strong forms of cross-sectional correlation that are normally ruled out in the standard SAR literature. Our framework is general enough to include as special cases also the random walk with a drift model, the local to unit root model (LUR) with a drift and the model for moderate integration with a drift.

We develop refined inference for spatial regression models with predetermined regressors. The ordinary least squares estimate of the spatial parameter is neither consistent nor asymptotically normal, unless the elements of the spatial weight matrix uniformly vanish as sample size diverges. We develop refined testing of the hypothesis of no spatial dependence, without requiring such negligibility of spatial weights, by formal Edgeworth expansions. We also develop such higher-order expansions for both an unstudentized and a studentized transformed estimate, where the studentized one can be used to provide refined interval estimates. A Monte Carlo study of finite sample performance is included.

The main purpose of this paper is to investigate the role of social and emotional learning (SEL) skills and resilience in explaining mental health in male and female adolescents, during the COVID-19 pandemic. Three self-report questionnaires were administered to 778 participants aged between 11 and 16 years (mean age = 12.73 years; SD = 1.73) and recruited from 18 schools in Northern Italy. The SSIS-SELb-S and the CD-RISC 10 assessed SEL and resilience skills respectively, while the Strengths and Difficulties Questionnaire (SDQ) was used to measure mental health in terms of internalizing problems, externalizing problems, and prosocial behavior. We found that SEL and resilience skills were positively and significantly associated with each other, negatively associated with internalizing and externalizing problems, and positively related to prosocial behavior. Three linear regression analyses showed the significant role of resilience, age, and gender in explaining the variance of internalizing problems; the significant role of SEL skills, resilience, age, and gender in explaining the variance of externalizing problems; and the role of SEL skills, age, and gender in explaining prosocial behavior. Importantly, we found that resilience fully mediated the relationship between SEL skills and internalizing problems, partially mediated the relationship between SEL skills and externalizing problems and didn't mediate the relationship between SEL skills and prosocial behavior. The paper concludes with a discussion of the limitations of the study as well as its practical implications.

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial or network interaction. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. The choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops an omnibus specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, functional form and the model itself. The approach extends the framework of conditional moment testing of Bierens (1982, 1990) to the general spatial setting. We derive the asymptotic distribution of our test statistic under the null hypothesis of correct SAR specification and show consistency of the test. A Monte Carlo study is conducted to study finite sample performance of the test. An empirical illustration on the performance of our test in the modeling of tax competition in Finland and Switzerland is included.

The concept of innovation in the educational field has assumed a growing importance over time as it is seen as a solution for a school that requires more and more quality in a constantly changing society. The literature review carried out in this contribution aims to provide the theoretical framework and state of the art needed to identify the dimensions for innovation, also in light of what is currently being done in Europe and internationally in this regard. The recognition and comparison of different validated theoretical frameworks allowed to build the foundations of the Framework for the evaluation of innovation processes, where the indicators were designed and adapted to the specific Italian school context, and to the vision of school innovation gained over the years.

The search for life and professional opportunities in a new country represents a significantchallenge for migrants, immigrants and refugees, in terms of developing knowledgeand skills for full social and professional integration. The massive migratory flowsof recent years have fuelled a growing attention of the scientific community on theprocesses of training, employment and integration of young asylum seekers from non-EU countries.The work presents some results of “CREI - Creating networks for immigrants” Project(AMIF funds 2014-2020). At the centre of the project there is the application of an educationaland career guidance model with qualitative-quantitative tools to recognise, validateand develop strategic skills of young foreigners.The result is a complex analysis which, on the one hand, helps people to know and managethemselves better, and on the other hand allows trainers to develop educational andvocational support programmes.

Ordinary least squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper explores the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (i) the indirect inference estimator displays little bias even in very small samples and gives overall performance that is comparable to the QML while raising variance in some cases; (ii) indirect inference applied to QML also enjoys good finite sample properties; and (iii) indirect inference shows robust performance in the presence of heavy tailed error distributions.

Ordinary least squares (OLS) is well-known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper explores the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator is robust to departures from normal disturbances and is computationally straightforward compared with pseudo Gaussian maximum likelihood (PML). Monte Carlo experiments based on various specifications of the weighting matrix confirm that the indirect inference estimator displays little bias even in very small samples and gives overall performance that is comparable to the Gaussian PML. Keywords; bias, binding function, inconsistency, indirect inference, spatial autoregression