Purpose This article examines the alignment between the skills demanded of future legal professionals and the curriculum mandated for Indian law degrees. It offers the first application of the future-skills cluster framework underpinning the QS World Future Skills Index to a single profession-facing discipline, using Indian legal education as the case. Design/methodology/approach A qualitative documentary analysis maps every component of the curriculum mandated by the Bar Council of India's Rules of Legal Education, 2008 against four future-skills clusters and digital/AI literacy, using a three-level coding scheme (direct, partial, absent). Curricular supply is then triangulated against employer demand evidence from the India Skills Report 2025, country-level alignment data from the QS World Future Skills Index 2027 and national labour-market analysis by NCAER. Findings Entrepreneurial capability and digital/AI literacy are structural gaps in the mandated curriculum; ethical instruction is present but confined to enrolment-oriented professional conduct; human-cognitive and leadership skills are exercised incidentally rather than taught or assessed as such. The discipline-level entrepreneurial void converges with the gap that QS independently identifies as India's largest national skills deficit. Legal education is additionally invisible in India's flagship employability measurement, removing the feedback loop through which other disciplines confront labour-market signals. Research limitations/implications The analysis maps the regulatory minimum rather than institutional practice, and demand-side evidence for the legal sector is inferred from economy-wide instruments. Both limitations define an agenda for primary research on legal employers and institutional curricula. Practical implications The findings support a periodic, market-referenced revision cycle for the mandated law curriculum, a mandated clinical paper on legal technology and artificial intelligence, relocation of entrepreneurial content into the compulsory core and structured practice pathways for legal academics. Social implications Access to justice depends on a steady supply of capable lawyers, many serving as sole practitioners in underserved regions. If artificial intelligence removes the junior work through which practitioners have traditionally developed, and curricula do not compensate, the resulting judgement deficit will fall hardest on litigants who cannot afford established counsel, widening existing inequalities in India's legal system. Originality/value The article contributes a replicable method for auditing a professionally mandated curriculum against future-skills frameworks, and advances the argument that artificial intelligence is dismantling the apprenticeship route through which legal judgement has traditionally been formed, converting curriculum reform from an employability question into a professional-formation question. The audit method is transferable: any profession or jurisdiction in which a statutory body mandates the curriculum can be examined in the same way, positioning the study as a case that speaks to curriculum evaluation across regulated professions.

Test Abstract

Harikrishnan S. brings a non-linear academic path to legal scholarship, having trained first in chemistry before moving into law. He is Assistant Professor at the School of Law, Governance and Public Policy, Chanakya University, Bengaluru, where he coordinates the undergraduate programme and student mentorship. He completed his LLB and LLM at the University of Calicut and his PhD at Alliance University, supervised by Prof. (Dr.) Mukul Saxena, and previously practised as an advocate. His scholarship combines doctrinal and empirical methods across tax law, public finance, fiscal constitutionalism, corporate and commercial law, and climate governance and Climate Finance, reflecting a continuing interest in how legal education itself must evolve to meet emerging economic and technological demands.

Hariprasad Manjunath is an Assistant Professor at the School of Mathematics and Natural Sciences, Chanakya University, Devanahalli. He earned his Ph.D. from the Department of Computational and Data Sciences at the Indian Institute of Science (IISc), Bengaluru, where his research focused on fast algorithms for computing eigenvalues of structured matrices, including periodic and Toeplitz-type systems. His doctoral work combined ideas from numerical linear algebra and matrix theory with applications to computational problems in engineering. Following his Ph.D., he held a postdoctoral position at INRIA, Université Côte d’Azur, France, where he worked on spectral methods for community recovery in networks and problems related to spectral seriation in random geometric graphs. He has published in peer-reviewed journals such as Applied Mathematics and Computation and Special Matrices, and has presented at several international conferences. In addition to academic research, he has briefly worked in industry, including contributing to the fine-tuning of large language models for domain-specific virtual assistants. He has also mentored student projects related to pattern detection in graphs, signal processing, and matrix computations. His teaching experience includes courses in linear algebra, discrete mathematics, and data science at both undergraduate and postgraduate levels.

Dr Gourab Mukherjee is an Assistant Professor in the School of Mathematics and Natural Sciences at Chanakya University. Prior to his current appointment, he served as a DST-INSPIRE Faculty at CSIR–Indian Institute of Chemical Technology (IICT), Hyderabad, and as a MOPGA Visiting Researcher at iSm2-BiosCiences, Institut des Sciences Moléculaires de Marseille, France. He obtained his Ph.D. in Chemistry from IIT Guwahati and pursued postdoctoral research at the Tata Institute of Fundamental Research (TIFR), Mumbai, and the University of Edinburgh, UK. He also held a Visiting Research Fellowship at the University of Manchester. Dr Mukherjee’s research lies at the interface of inorganic and bioinorganic chemistry, with a focus on the design and mechanistic investigation of transition metal complexes relevant to biological and catalytic processes. Some of his works have been published in reputed international journals.

The knowledge of landscape dynamics aids in evolving strategies for the prudent management of natural resources to sustain ecosystem services. The availability of spatiotemporal remote sensing data with advancements in artificial intelligence (AI) and machine learning (ML) algorithms has aided in assessing the ecological status in urban environments, markedly revealing complex patterns and interactions. The current communication presents landscape dynamics in the Bengaluru Urban district from 1973 to 2022 using a supervised machine learning technique based on the Random Forest algorithm with temporal Landsat data, which showed a 51.86% increase in the built-up area and a 26.28% decrease in the green cover. Rapid unplanned urbanization after globalization and the opening up of Indian markets (in Bengaluru city) has witnessed erosion in the natural surface (waterbodies and green cover) in the neighborhood, which has been impacting the health of the environment and people. Computation of fragmentation indices showed a decline of the native green cover by 177.2 sq. km. in the southern part of the district. Likely land use changes are predicted using the Cellular Automata Markov model considering the base case scenario. The analyses revealed a further possible increase in built-up to 1536.08 sq. km, a decrease in green cover by 14.32 sq. km by 2038, and the disappearance of water bodies, which highlights the need to mitigate the adverse impacts of land use changes through planned urbanization considering the environment and livelihood of local communities. The decline of heat sinks such as water bodies and green cover would contribute to an increase in the land surface temperature (LST), which would affect the microclimate of Bengaluru, highlighting the need to sustain ecosystem services to support the livelihood of local communities. Understanding the ecological significance of diverse habitat characteristics of the urban region and the prediction of likely changes in a high degree of spatial heterogeneity would assist the decision-makers in framing appropriate policies.

Background: Studies have indicated that mothers are essential primary informants for adolescent girls regarding menstruation. Their knowledge and comfort levels regarding this subject considerably influence the information that their daughters receive. In addition to influencing the daughters’ initial experiences of menarche, maternal attitudes towards menstruation also affects their broader perceptions of menstruation throughout adolescence. This study aims to investigate mothers’ attitudes regarding menstruation, focusing on mother-daughter conversations and their influence on girls’ menstrual health education and experiences. Method: A phenomenological research design using the focus group discussion technique was employed to procure data. Fifty-three participants were recruited using organisational and advertisement techniques of purposive sampling. Six focus group discussions were conducted with participants’ spoken language. The audio recording was translated and transcribed verbatim into the English language. Using Atlas.ti software, a qualitative research analyst performed thematic analysis on verbatim transcripts from discussions in six focus groups. Outcome: The results of the study have unveiled thoughts and feelings of mothers anticipating their daughter’s onset of menstruation. It highlights that mothers’ perceptions are largely shaped by their personal feelings, especially surrounding issues of safety, protection, and the daughters’ ability to understand and manage their menstrual health. The findings offer a detailed insight into the mothers’ challenges, their support systems, and their psychological and emotional states as they navigate this significant transition for their daughter’s approaching puberty. Recommendations: A comprehensive instrument is necessary to assess attitudes around menstruation, enabling the identification of ways to provide mothers, women, and girls with vital information and assistance, thereby fostering positive body acceptance.