The relationship between adolescents and digital technologies is a widely debated topic, often framed in terms of prohibitionist approaches on one hand, and digital empowerment on the other. However, as digital engagement becomes an integral part of everyday life, fostering critical thinking and responsible media use appears more effective than restrictive strategies. This paper presents a Media Education project aimed at empowering adolescents in their use of digital tools. The educational intervention was conducted in a lower secondary school, involving 238 students, and included interactive classroom sessions, debates, and playful activities such as video games and avatar creation. The pedagogical approach prioritised active student participation over traditional top-down instruction. A final questionnaire assessed both student satisfaction and the skills acquired. Results from correlation analysis and MANOVA indicate that interactive and playful activities significantly contributed to overall course satisfaction. These findings support recent theories suggesting that playful, participatory learning environments enhance students’ motivation and interest, promoting their immersion in educational contexts.
The unregulated proliferation of hate speech, defined as any expression of beliefs that incite hatred, discrimination, or violence against individuals or social groups (Fortuna & Nunes, 2018), in social media has called into question the efficacy of removal-based content moderation and has driven the need to explore alternative approaches. AI-generated counterspeech could be a scalable and cost-effective intervention (Ashida & Komachi, 2022). Counterspeech is defined as a direct response to hate speech aimed at refuting or undermining it (Wachs et al., 2023). Research in this growing field is fragmented across computational linguistics and social sciences, lacking systematic methods to define and evaluate effective counterspeech, revealing a gap between AI's technical capabilities and evidenced social impact. This review aims to examine evidence from these two fields to identify the differing conceptualizations of counterspeech, map the collaborative approaches used for counterspeech generation and evaluation, and examine the alignment between stated research goals (e.g., reducing hate speech in digital environments) and the operational measures employed to identify them. Only a few studies evaluate counterspeech in real-world interactive settings or measure long-term impact. We identify some key misalignments, such as the reliance on text metrics and human or artificial annotators as proxies for complex social interventions, instead of integrating psychological and behavioral measures. We propose a linked approach to counterspeech that links theoretical frameworks, procedures, measurements and research goals from the perspective of both psychology and computational linguistics to identify promising directions for future interdisciplinary collaboration and more socially meaningful AI-assisted interventions.
As artificial intelligence (AI) systems prove their utility in various aspects of daily life, their potential risk to societal progress is still debated given the increasing rates of their discriminatory behaviors. Some preliminary evidence suggests that the same discriminatory behavior may be perceived differently when performed by a person or an AI system, yet it remains largely unknown how people perceive and respond to AI’s discriminatory behaviors. Through two studies conducted in Türkiye (Study 1) and the United States (Study 2), participants read about either AI’s discrimination against women, humans’ discrimination against women, or an unrelated topic, and indicated the degree of perceived threat to women, willingness to participate in collective action for women’s equal treatment, and their attitudes toward women and feminists. In both cultures, discrimination was perceived to be less threatening to women when committed by AI compared to a human, yet collective action intentions and attitudes toward women and feminists did not differ across conditions. Notably, American men consistently reported less favorable attitudes toward women after reading about discrimination. These findings suggest discrimination by AI requires special attention and underline the importance of investigating the influence of technologies on sustaining intergroup inequality.
Online hate speech against school students is increasing globally. This paper examines how digital literacy programmes and school interventions can help students aged 10 to 18 recognise, resist and respond to hate speech in online spaces. The study reviews research published between the years, 2020 and 2026. It expatiates the causes and effects of online aggression among adolescents. Qualitative data gathered through structured thematic discussions with 16 educationists drawn from government, aided and unaided schools across the State of Kerala. Inductive thematic analysis of these discussions informs the practical approaches presented, drawing on Social Emotional Learning (SEL) and critical media literacy frameworks. The paper highlights several initiatives used in Kerala schools. These include the KITE fake news detection curriculum, the State Council of Educational Research and Training (SCERT) value education framework, the Student Police Cadet programme and the Suraksha Mitram support system. Two conceptual models guide the discussion. The first model explains how films, games and digital media can increase hostility and aggressive behaviour among students. The second model presents a four-component resilience framework that helps students develop responsible digital behaviour and stronger critical thinking. This framework is explicitly exploratory and normative in character: it synthesises existing evidence and programme documentation into a prescriptive architecture designed for iterative evaluation and refinement. The paper recommends a multi-level prevention approach. It focuses on student skill development, positive peer influence, teacher training, family participation and clear school policies. The approach suits government, aided and unaided schools in Kerala.
The research addresses politically motivated hate speech in Albanian-language triggered by recent political events. An evident reactive hate dominates the comment section of political news outlet in social media, so that one is moved to detect the underlying partisan motivations of this hostility. Some comments provide answers and others suggest reasons that reflect a spreading animosity towards politics and its traditional or emerging actors. Robert Entman’s frame paradigm offers a theoretical foundation to detect hate speech embedded in mediated interaction in digital platforms. This research combines framing with interpretative insights derived from critical discourse analysis to capture deeper causal logics embedded in frames as a communicating text. Findings reveal that politically motivated hate speech is often normalized and legitimized in digital interaction when it is frames around some thematic issues such as national identity, moral values, economic development. Hate speech targets include political actors and institutions, collective outer groups in some cases. Recurring narrative patterns highlight responsibility attribution and negative moral evaluation, which reinforce shared perceptions about political legitimacy, and underlying legacy. The research suggests that a context-sensitive approach when addressing hate speech detection helps to understand mechanisms of its construction in order to design preventive initiatives.
